I've been thinking about buying a system to run LLMs locally but the price for one that'll run Qwen3.8-27B well is quite offputting to say the least.
What I've been looking at instead is inference providers that use TEE and E2EE to provide cryptographic guarantees that my prompts and responses are only visible to me and the GPU itself.
Despite their docs and assurances of what their guarantees mean, I'm having trouble getting to a point where I'm actually comfortable trusting them with secrets though. Phala for example seems to be E2EE only to the gateway and will then forward prompts to (potentially third party) providers.
Has anyone been down this path and found a provider they feel safe with?
Not suggesting a provider but if you're willing to get a Chinamod GPU, go get RTX 3080 20G or RTX 2080 Ti 22G. Get a couple of them and you can probably run Qwen3.8-27B at a reasonable speed.
I've got a RTX 3080 20G for $450 like a year ago. With llama.cpp, bf16 kv cache, kv cache offloaded to RAM, Qwen3.8 27B UD-Q4_K_XL, single RTX 3080 20G, I got 10 tok/s initially and it dropped to 5 tok/s at 50k context. I'm thinking of getting another Chinamod GPU.
Building a dual Chinamod GPU machine would only cost like $1000~$1500. It's not too bad compared with the alternatives!
Or, theres the option of a 32gb v100 (about $600USD on taobao etc), where you can get 1200 of prefill at 80 of decode: https://github.com/geoffwatts/ninfer-v100 - that's _really_ cheap inference, and it's not a modified card - you just need to add a blower or water block.
Well, yes me. I was on the same path. Researching hardware and coming to the same conclusion. I found tinfoil.sh which looks promising?
My current solution is a private ChatGPT-like interface using OpenRouter’s API with Zero Data Retention enabled. Not perfect or verifiable but I think it’s acceptable for now.
Qwen 3.8 has the same architecture and the same parameter count as Qwen 3.6. Something is not right with the GGUF if it's 2 times slower. The post says "The hybrid attention architecture is new" and says the author's older Llama build from a "couple weeks ago" failed to run Qwen 3.8 because it did not support Qwen35 architecture, but both 3.6 and 3.8 are based on Qwen35 which was released in February 2026. The post doesn't make any sense.
I'm encountering the same behavior. I've tried 4-8bit quants and get 14-17 tok/s with one run that achieved 19. I'm eagerly awaiting dflash2 support in Unsloth or LM Studio, as allegedly that should increase throughput to around 30tok/s, which is the baseline for what I consider at least somewhat interactive.
Jealous of the folks with 5090s running ninfer and getting >100tok/s. At those speeds it's a true frontier replacement IMO.
That isn’t right. Use oMLX or something similar to serve the model with internal MTP enabled. You should get at least 40 tok/sec, but I’m not sure what hardware you are using. Dflash will mess up batching, not really worth it. You need to sift through hugging face for the right model though, and some of the MTP models are meant for rapid MLX and not oMLX.
You and I have the same machine. Do you mind sharing the model ID you're using? I'm on oMLX also but I haven't seen anything above ~20 tok/s out of 3.8 27B, even with MTP and generating code.
https://huggingface.co/Jundot/Qwen3.6-35B-A3B-oQ6-fp16-mtp is my favorite right now (and nothing else is even close), you should be able to get 80-90 tok/sec enabling MTP and disabling turboquant. Supposedly the 6-bit quant is essential for decent MTP performance, although I didn’t test the 4-bit quant.
for 3.8? I can get 40 tok/sec on it (M3 Max 64GB), but I don't use it because I can get 90 tok/sec with 3.6 MoE (MTP + 6 bit quant), and I don't notice any quality improvements for my tasks using a dense model.
Did you ask Gemini or DeepSeek to look at your oMLX server log to see what was going on? This can help a lot if it is just a misconfiguration.
Here's reality, MLX on the software layer will not magically place hardware matrix multiplication units in your GPU cores.
Newer Macs are always just gonna smoke anything earlier than an M5.
Not even sure why this person is trying to get this stuff to run on hardware that wasn't designed for AI?
Everyone is pointing him to newer hardware precisely because you need the newer stuff to get models to be performant. You can go with AMD, NVidia or Apple, but you're gonna be using stuff designed well after the M3 if you want to push >100tok/s.
> Not even sure why this person is trying to get this stuff to run on hardware that wasn't designed for AI?
Posturing/overclaiming like this shades rather than illuminates, there are no worlds in which the "M3...wasn't designed for AI". My M4 Max 64 GB gets the same speed.
I run similar workflows as the author on my 5090. It's really good and reasonably fast at ~90 tok/sec on LM Studio. I haven't tried ninfer yet. The only problem is having to be mindful about the context size. I am jealous of the folks with RTX 6000.
Ninfer is the real deal. 160 tokens/sec. You can parallelize 4 chats at once and get ~400 tokens/sec I’ve heard. Although that just compounds the context size issue, still absolutely incredible for running locally. I made a web based battle chess game with sound effects running on a pi harness in about 15 minutes, complete with a (very unskilled) “AI” (not an llm) that plays against you if you want!
Pretty impressive how the Mac ends up less expensive than the Strix Halo boxes, at least here in Ireland. A 128GB Mac Studio with an M5 Max (the Ultra can only have 96 or 256GB) still costs less than the "GMKtec EVO-X2" or the Nvidia DGX Spark with similar performance. Is it the same in the US?
That's not right. Mac Studio M5 Max 128gb, in Apple Store Ireland, starts from 5,909e (~6,847 USD; tax included). On the other hand, GMKtec Evo-X2 128gb can be currently purchased for 3,299e (~3,822 USD; tax included) from GMKtec online store (free shipping from DE warehouse). It's quite the difference in price.
I figured it's dodgy amazon ie listings. DGX Spark/Asus Ascent GX10 prices are inflated there too. They'll also be happy to sell you an RTX 5090 for 10 grand.
I am really surprised you’re seeing half the generation performance of 3.6 with 3.8 at the same parameter size and quantization (and same prefill performance to boot) - is there just an optimization in the stack somewhere for 3.6 that hasn’t landed yet for 3.8?
Curious if other folks are also surprised by this apparent discrepancy or have a ready explanation.
I suspect this might be due to MTP mispedictions. Either because the 3.8 quantized model weights do not include MTP heads, or as what happened with Ornith-1.5 recently, corrupted MTP heads, or due to a software issue in Ollama.
I remember an article a couple weeks back where someone used an HPE server with two older Xeon E-series CPUs and got reasonable performance by compiling llama with optimal switches for the architecture (memory alignment, page sizes, etc). I was impressed because those Xeons only had AVX2. If you are willing to spend a little more, you can get a slightly newer Xeon with AVX-512 and 4 sockets.
I can't find the post though.
Edit: a quick search from a server refurbisher nearby gives me a dual Xeon Gold 6330 28-core machine with 512GB and a 16GB V100 GPU. The memory is spread across 32 slots, for about £6,470.
the fact that this author cannot get qwen3.8-27b run at the same speed as qwen3.6-27b, says the article is not worth reading. the author does not know anything about how to run local AI. 3.8 and 3.6 are the same model with different weight.
both tg and pp speed are so terrible on author's machine.
Yea Qwen3.8 wasn't fun to use on my 64GB M4 Max either (better than these numbers though), so my new daily driver is Ornith-1.5-35B-A3B-MLX-4bit. I recommend giving that a whirl if you're on similar hardware, it's definitely better than Qwen3.6 35b-a3b which was my go-to before.
I tried Ornith-1.5-35B-A3B-MLX-4bit on a 32G M2-Pro mac mini with pi and OpenCode. I didn’t get very good results coding in Python, Racket, and TypeScript. I have seen several positive comments like yours so I was probably doing something wrong. I amgetting the new 64G mac mini in 4 weeks, and I made a note to try Ornith-1.5-35B-A3B-MLX-4bit again.
Can you tell more about your experience with Ornith? I've come across it, and the benchmarks on its landing page are unbeleavably good. But it isn't featured on more established benchmarks like ArtificialAnalysis and it's not on OpenRouter, so I wrote it off as scam.
I am running Ornith 1.5 Q4 on my work issued laptop which surprisingly has 40gb of RAM and 20GB of “unified” VRAM.
I am getting 50t/s of prefill and 5-6t/s of inference.
You can’t get crazy with it, but it’s pretty nice to just let it work away at simpler prompts overnight or during lunch break or while I am working on other things.
Being able to literally a 30gb blob and then being able to talk with it while running on your machine it’s a different kind of magic compared to a far away magic in the cloud.
The cheapest card that will run this model very well is a ln unlocked CMP 170HX. But you can run it on a 3090. I run it on an old spare A6000 Ampere. I think I wouldn’t use anything lower than 60 tok/s though, which you can get with MTP etc. I just use a full vllm stack but some people see a lot of speed with ninfer (there are non 5090 ports).
The large RAM Macs are unusable for inference of dense models as of now. Token generation is too slow.
These numbers are a lot lower than I expected from such pricey hardware.
I'll stick with my old AMD datacenter cards thanks. An Instinct MI50 32GB would cost around $550 or so. It has 1TB/s vram bandwidth thanks to its HBM2.
I am also running some old AMD datacenter cards, 2x MI25 in my case. Getting around 30 tokens/second with short context.
Tensor parallel in llama.cpp using RCCL (disabled by default in llama.cpp for some reason). Surprisingly, for these cards HIP is actually faster than Vulkan, unlike the 9070 XT where Vulkan still wins.
ROCm nightlies do actually support these old cards, just not the ROCm stable releases.
I use Llama.cpp and OpenAI-style API of course (who doesn't, unless you use Ollama perhaps?)
Ollama sucks, they dropped ROCm support for my card out of the blue, and the Vulkan replacement wasn't ready. The developer just shrugged and didn't care about the people affected so I moved to llama-server. Also, here's some other reasons to avoid ollama: https://sleepingrobots.com/dreams/stop-using-ollama/
And really, ollama are trying to sell their cloud platform and are always running behind llama.cpp in new features like the KV Cache quantisation.
So numbers, I don't really measure it from day to day, I'm not really interested in benchmarks at all. But I just did a quick test, llama 3.1 8b q8_0 does about 70tk/s (with Vulkan up from 45 when I was still running ROCm), and qwen 3.5 9b q8_0 does about 45, I think that includes its time spent thinking, not sure. Overall I'm happy with that.
Smaller sizes and quants are of course faster than this.
It's not terribly much faster than a Mac Studio, but it's one hell of a lot cheaper of course. And for the price of a Mac Studio you can get a lot faster hardware. The only thing where the studio excels is if you need large amounts of vram like 64GB and up and run the models to go with that (I run multiple small ones and spread them over multiple cards)
How so? I've had no issues running llama.cpp with vulkan compute on my AMD graphics card (9070 XT). llama.cpp exposes an OpenAI API endpoint, which seems to be the lingua franca for AI applications. Does llama.cpp not work with datacenter AMD graphics cards?
Yes it works great with them through Vulkan. ROCm is more hit and miss, especially because the cards which are affordable (and PCIe, the new ones aren't PCIe compatible!) are already fairly old and have already been dropped by ROCm.
But Vulkan is actually faster too so there is no reason to bother with ROCm anyway.
I don't currently have an actual datacenter card, but I have a Radeon VII Pro, which is basically a 16GB version of the MI50 Instinct with its own fan.
Apart from learning, I cant see the point of spending that kind of money and energy to get such awfull token generation speed. Assuming memory bandwith is the bottleneck, is it just a matter of time until we start to see hbm4 based chip able to run qwen3.8 for normal people running at more than 500 token / seconds? Is memory speed the only technological bottlenecks that prevent us from having fast local model?
For me, a HUGE benefit to running local models is that my data stays mine, on my computers only.
Even if Google, OpenAI, Anthropic whatever promise not to use it, how sure can I be of that? Data is gold anyway. And we're in the middle of a massive gold rush. And they've already been caught scraping sites they had no business to, and pirating books. Clearly their promises and the law mean nothing to them. It's just something you pay off in a settlement if you get caught, a cost of doing business.
With local models besides the speed you also lose a lot of inference quality but it helps to mitigate that. For example making sure your RAG inputs are properly prepared and categorised so the model can find them easily without having to wade through a bunch of misdirected crap.
For example what I do with my bookmarks and chats (the latter are recorded per day), is before I enter them into a RAG corpus I run a small LLM over it to summarise what's being discussed or what the webpage is about. That really helped retrieval quality, and doing this is a batch task that can run asynchronously so speed is not very relevant. This way I get a lot closer to SOTA-model retrieval quality (like with Office Copilot 365 looking for conversations in Teams).
PS: I wouldn't be surprised if Microsoft runs something similar on their end :)
But yes the Mac Studio is outrageously priced especially for running such a small model as qwen 27b. For that price you can use something much much cheaper. It only shines for models that are much bigger, because there simply is not much hardware that can address fast 512GB banks.
Which is a regulation constraint on many professions, BTW. Some people simply can't give some data to say, ChatGPT, without comprehensive guarantees written in Sam Altman's blood.
Guarantees that are completely worthless, because these companies will simply pay the fine for breaking their word.
There need to be second order consequences to those who delegate their responsibility to companies that behave like this. They know the contract is worthless, but then proceed to use it as defense for their own gross negligence.
Yes that's cool for a company but not when I'm the end user, because it's my data that's being misused. It's my problem and having someone to blame doesn't actually solve the problem.
My original point was that, for many professionals, there is a strong incentive to go local with their AI models the same way you wouldn’t be able to use cloud storage without the assurance the data would be physically stored within a specific region.
I'm not sure. I don't use Mac anymore. It used to be my daily driver but they pushed me away a few years ago with the constant iOSification. I've heard good thing about LM Studio, that's about it. https://lmstudio.ai/
I just run a server with Linux (previously multiple servers but I found a way to add multiple GPUs to a single one).
Similar here, except I used Opus 4.8. I don't mind using cloud models to make code as long as I don't put actual private data into the model. And I can't run qwen 27b reliably at the moment, I need a better gpu.
It was pretty easy mode like this tbh, though you do need to know what you're doing. I use it with llama-server and openwebui.
I guess you can get it a lot more click to go with something like LM Studio though but I want to use it on a server and call upon its services from multiple sources.
The M3 Ultra was a lot cheaper for most of its lifetime, and really the main reason for buying it is if you want a lot of unified RAM, to run bigger models than 27B models.
The new M5 Ultra should deliver ~50% faster token generation (1.2TB/s mem bandwidth); and extrapolating from my M5 Max (since the M5 Ultra is literally just 2x Maxes), probably ~3x faster PP.
But I don't think it's fair to look at this only from monetary ROI vs API. With local models, you get privacy and ownership.
I do not trust _any_ API provider with my most personal information; such as for example, all my messages, emails, daily journals spanning a decade+, all my photos and videos, etc. So it unlocks new use cases that I simply don't feel comfortable with via API.
And a personal assistant with ALL my context and data, locally, has been incredibly useful for me :) Zero outages either, zero "overloaded", etc. Nearly-zero refusals too (I don't run abliterated models; thinking prefill has worked for anything I've wanted to do)
I have 2x 3090s and I get 260 tokens/sec peak (110 avg) with Dflash2 and about 1500t/s prefill with a Q4 quant of Qwen 27b. I didn't buy an overpriced Apple product and it performs much better. It's extremely reliable for Agentic coding and I can run 2-3 simultaneous agents with a full ~260k context window.
I realize the price of NVIDIA has gone up but there are plenty of GPU options from others like AMD and Intel with reasonable performance.
A slow AI tasks that can process my self hosted personal journal, my medical history in fasten, my diet and exercise in Mealie and Sparky Fitness to offer insights once a day or even once a week is better than nothing. Because I would never upload that data to any of the AI companies.
Ornith 1.5 35B MoE is very close in most benchmarks, but runs at 100 tk/s where Qwen3.8 27B runs at 30 tk/s (MBP M5 Max 48GB). It's now my go-to model for quick inquiries. Ornith 1.0 had problems with world knowledge, but 1.5 seems very solid on that front. I'll be running some agentic tests today.
Ornith 1.5 35B is so fast that my CPU doesn't even have time to heat up, where Qwen3.8 would turn my laptop into a toaster within a few minutes. I hope agentic tasks will hold up!
I am also seeing slower speeds, roughly the same ballpark, sometimes even lower - 10-11 tok/s on M5 Max. If there is that ONE version (GGUF or MLX) that runs roughly as fast as 3.6 used to run, please let me know.
What would be incredible is the 3.8 35B MoE version too, I can run 3.6 with 60 tok/s which is a really, really nice speed.
When someone says stuff like this what does it mean? A Mac/computer with this much RAM capacity/marker (how would this even be useful in this case?) or 16 or ≈16GB free RAM?
Because RAM usage at least on my Mac is another beast altogether and seems nothing will bring down the RAM usage other tham <rm -rf>ing the / (that is if I can and something survives to notice the miracle)
The local LLM scene needs a Draw Things equivalent for Mac. Too much fiddle for things that doesn't make sense (Qwen 3.8 27B should be exactly the same speed as Qwen 3.6 27B). It feels like that I am teasing (I am the author of Draw Things) something, because it is.
What you get with Draw Things: 1. Download the app from Mac AppStore; 2. Download the model; 3. Tap "Try recommended settings", have the guarantee that for whatever model it supports, it is the fastest in Mac ecosystem, no need to fiddle.
What you get with local LLM options: 1. Download the inference engine app from the web; 2. Download the model; 3. Configure MTP / DFlash / DSpark whatever; 4. Configure your Pi / OpenCode harness to point to this local LLM inference engine. 5. Configure tools for these harness to be effective. 6. Switching between Ollama, LM Studio, llama.cpp (DwarfStar4), oMLX, MTPLX, to see which one is fastest for your workload. 7. Again switching between different quants of the same model to see which one is less dumb.
To be honest, llama.cpp probably the closest to deliver on "just use it, don't worry about speed" if your focus is about a pure LLM inference engine.
I’ve casually used Draw Things a couple of times (great tool!), and I did not find it beginner friendly. I remember feeling a lot of doubt, confusion, and frustration.
Am I getting these results because I picked the wrong model? Or I need to improve my prompt? Or the tool just can’t do what I’m trying to do? How current are these model recommendations? Have they been superseded by something newer?
As a true beginner to AI at the time, even the sizes and bits were meaningless to me. And I don’t remember having any context as to what I should be attempting to run on my mac.
So I think you need to include steps 4, 5, and 6 of swapping between different models, quants, and prompts. And step 7 is probably wading through the complicated UI, full of jargon that most people don’t know.
Don’t get me wrong, I recognize that it’s a powerful tool, and the steep learning curve exists because it exposes quite a few power-user features. But for someone graduating from commercial AI image generators that take a text prompt and maybe a choice of couple models, it’s not easy.
Yes, I heard you! I think one of the issue Draw Things inherited is the baggage of supporting too much models. Once you settled on a model, then it is just "try recommended settings", and prompt.
The model part is unfortunate, but luckily converging now.
On the LLM side, hopefully it is not an issue too as long as getting aggressive at pruning models.
This low-effort slop post is misleading. It suggest that 3.8 is half as fast as 3.6, but this is almost definitely because MTP isn't enabled by default. The two models should perform the same - they are for me. When you get AI to think for you, you lose.
FWIW, on my Mac Studio I get ~24-27 tok/s generation between 0-16k context in - that's on the Q6_K GGUF with speculative decoding on. I have spent zero effort optimizing/improving this so far but will be trying the 4 bit MLX next (I've tended to find models drop off somewhat below 6 bit but maybe that isn't the case nowadays).
That seems like an expensive way to block ads considering traditional ad blockers have been doing that with a tiny fraction of the compute for decades.
Have you used a social network in the past year? Facebook, YouTube, Pinterest, et al. have become increasingly hostile to blockers with techniques that break pattern-matching on hostnames and element selectors. It may be inevitable that adblockers move toward visual identification.
There are many, many people who are betraying a lack of facility with these models.
Why is anyone trying to run these on an M3?
I thought it was common knowledge that, if you insist on using Apple, only M5 processors and higher have matrix multiplication units in the cores?
I've seen the same thing with people buying NVidia cards with 16GB of ram and wondering why they aren't getting 100tok/s?
Guys, please, be reasonable. You'll have to get the hardware if you want to run these things fast. If you want to experiment, the slow stuff is fine. But try not to get on HN and ask why your M3 can't get 100tok/s. You're kind of out-ing yourself.
Peaks at over 300 tok/s on my 5090 with Dflash2. At maximum context (252000 or so) still get 60 tok/s. And I don't have n-gram optimization working yet, I only have 64G memory.
>I realized I’d been sitting on the one thing most of those threads were missing: a machine that can actually run it properly, and time to measure it.
This was written by AI I take it. The one thing, huh? All those idiots running it on their RTX 6000s at 140tok/s must be feeling pretty stupid for not getting a Mac Studio instead.
> Qwen3.8 27B (Q4_K_M, 17GB) generates at ~14 tokens/s on my Mac Studio M3 Ultra
>~14 tokens/s
For anyone reading that has never ran local llms, please understand that anything under 100 tok/sec is worthless. You are faster typing stuff into Gemini free version that you get with a google account and copy/pasting it in (and you can easily build browser automation with playwright or any other js runtime to have this available in a chat window)
I get 80 tok/s on the same model, and it's pretty usable. I'm not chatting with it; it's either given a bag of tokens to generate an answer or it's doing some agentic coding.
The standard is not what you can do with it, the standard is what is the free alternative. Local LLMs need to be able to beat the rate limits on all the free models like Gemini to be useful. The only way to do that is to have high enough tok/sec, especially for agentic loops.
30 is worth it on a meaningfully better model, though annoying. 50 is pretty much unnoticeable (good old 60 FPS). 80-100 is heaven (aka 120 FPS - once you get used to it, it does suck to go back).
I would still rather have 30 tokens per second of GLM 5.3 Flash than 100 tokens per second of Qwen 3.6 35B-A3B.
I agree that 14t/s is pretty tedious for interactive use, yes. But 50-60tk/s is faster than I can read. 100tk/s is outright fast. Don't forget there is a limit entering content into meatspace.
Also, Gemini may be free but what if I don't want to give all my data to Google? This is precisely why I have a lot of stuff locally.
And will it remain free? How are they going to make back all those trillions of investment?
But yeah I would kinda balk at 14tk/s too that's why I use old datacenter/workstation-class GPUs.
I wouldn't say worthless but once you get used to 100+ tokens / s, it's really a visible slowdown, especially when running multiple agents acting on something more than basic prompt processing.
Still, as you said - for day to day, 50-60 tokens / s is a good baseline.
In agentic scenarios, an LLM has to read far more tokens than it outputs. I think focusing only on the decode speed is somewhat misleading. 14 tok/sec for decode is actually okayish. 93 tok/sec prefill is what's abysmal, my RTX 5090 goes above 2000 tok/sec with 5 bit quants.
I've been thinking about buying a system to run LLMs locally but the price for one that'll run Qwen3.8-27B well is quite offputting to say the least.
What I've been looking at instead is inference providers that use TEE and E2EE to provide cryptographic guarantees that my prompts and responses are only visible to me and the GPU itself.
Despite their docs and assurances of what their guarantees mean, I'm having trouble getting to a point where I'm actually comfortable trusting them with secrets though. Phala for example seems to be E2EE only to the gateway and will then forward prompts to (potentially third party) providers.
Has anyone been down this path and found a provider they feel safe with?
Not suggesting a provider but if you're willing to get a Chinamod GPU, go get RTX 3080 20G or RTX 2080 Ti 22G. Get a couple of them and you can probably run Qwen3.8-27B at a reasonable speed.
I've got a RTX 3080 20G for $450 like a year ago. With llama.cpp, bf16 kv cache, kv cache offloaded to RAM, Qwen3.8 27B UD-Q4_K_XL, single RTX 3080 20G, I got 10 tok/s initially and it dropped to 5 tok/s at 50k context. I'm thinking of getting another Chinamod GPU.
Building a dual Chinamod GPU machine would only cost like $1000~$1500. It's not too bad compared with the alternatives!
Or, theres the option of a 32gb v100 (about $600USD on taobao etc), where you can get 1200 of prefill at 80 of decode: https://github.com/geoffwatts/ninfer-v100 - that's _really_ cheap inference, and it's not a modified card - you just need to add a blower or water block.
> single RTX 3080 20G, I got 10 tok/s initially
Add $200, buy a used 3060 and you won't need to offload cache to RAM. Yo'd have like 50-60 t/s with MTP enabled.
aww. I used to have a 3060 and I upgraded to this 3080 20G.
Maybe it's time for me to get a 3060 back again. :P
I run qwen3.8 on 5060ti 16G RAM. If you can do with Q3 and 64k of context. It works great at 25t/s
A year ago was prior to the memory cartel. You're probably off by 2x.
I've checked. I can still get a 3080 20G for $525, or 2080 ti 22G for $380. The price has increased but not too bad.
Probably the modding had prevented it from gaining too much price increase.
Well, yes me. I was on the same path. Researching hardware and coming to the same conclusion. I found tinfoil.sh which looks promising?
My current solution is a private ChatGPT-like interface using OpenRouter’s API with Zero Data Retention enabled. Not perfect or verifiable but I think it’s acceptable for now.
https://news.ycombinator.com/item?id=43996555
https://openrouter.ai/docs/guides/features/zdr
Qwen 3.8 has the same architecture and the same parameter count as Qwen 3.6. Something is not right with the GGUF if it's 2 times slower. The post says "The hybrid attention architecture is new" and says the author's older Llama build from a "couple weeks ago" failed to run Qwen 3.8 because it did not support Qwen35 architecture, but both 3.6 and 3.8 are based on Qwen35 which was released in February 2026. The post doesn't make any sense.
I'm encountering the same behavior. I've tried 4-8bit quants and get 14-17 tok/s with one run that achieved 19. I'm eagerly awaiting dflash2 support in Unsloth or LM Studio, as allegedly that should increase throughput to around 30tok/s, which is the baseline for what I consider at least somewhat interactive.
Jealous of the folks with 5090s running ninfer and getting >100tok/s. At those speeds it's a true frontier replacement IMO.
That isn’t right. Use oMLX or something similar to serve the model with internal MTP enabled. You should get at least 40 tok/sec, but I’m not sure what hardware you are using. Dflash will mess up batching, not really worth it. You need to sift through hugging face for the right model though, and some of the MTP models are meant for rapid MLX and not oMLX.
You and I have the same machine. Do you mind sharing the model ID you're using? I'm on oMLX also but I haven't seen anything above ~20 tok/s out of 3.8 27B, even with MTP and generating code.
https://huggingface.co/Jundot/Qwen3.6-35B-A3B-oQ6-fp16-mtp is my favorite right now (and nothing else is even close), you should be able to get 80-90 tok/sec enabling MTP and disabling turboquant. Supposedly the 6-bit quant is essential for decent MTP performance, although I didn’t test the 4-bit quant.
I was just trying that yesterday on my M4 Max with the 6bit quant. It started at 40 tps but dropped to 10 once the context loaded up.
for 3.8? I can get 40 tok/sec on it (M3 Max 64GB), but I don't use it because I can get 90 tok/sec with 3.6 MoE (MTP + 6 bit quant), and I don't notice any quality improvements for my tasks using a dense model.
Did you ask Gemini or DeepSeek to look at your oMLX server log to see what was going on? This can help a lot if it is just a misconfiguration.
Just did the fun thought experiment of playing back this comment thread in my head ten years in the past and it's amusingly incomprehensible.
What’s the context size?
128k, 256k is also possible, but my tok/s drops off and the performance isn’t good.
Well, yes and no.
The author has an M3.
Here's reality, MLX on the software layer will not magically place hardware matrix multiplication units in your GPU cores.
Newer Macs are always just gonna smoke anything earlier than an M5.
Not even sure why this person is trying to get this stuff to run on hardware that wasn't designed for AI?
Everyone is pointing him to newer hardware precisely because you need the newer stuff to get models to be performant. You can go with AMD, NVidia or Apple, but you're gonna be using stuff designed well after the M3 if you want to push >100tok/s.
I would say that an M3 Ultra was designed for the LLMs around that time, plus Apple was pushing hard towards MLX over time.
> Not even sure why this person is trying to get this stuff to run on hardware that wasn't designed for AI?
Posturing/overclaiming like this shades rather than illuminates, there are no worlds in which the "M3...wasn't designed for AI". My M4 Max 64 GB gets the same speed.
I run similar workflows as the author on my 5090. It's really good and reasonably fast at ~90 tok/sec on LM Studio. I haven't tried ninfer yet. The only problem is having to be mindful about the context size. I am jealous of the folks with RTX 6000.
Ninfer is the real deal. 160 tokens/sec. You can parallelize 4 chats at once and get ~400 tokens/sec I’ve heard. Although that just compounds the context size issue, still absolutely incredible for running locally. I made a web based battle chess game with sound effects running on a pi harness in about 15 minutes, complete with a (very unskilled) “AI” (not an llm) that plays against you if you want!
Just tried it, really cool and works as advertised.
dflash2 is atmost 10-20% above mtp, it won't get you to 30 tps
Pretty impressive how the Mac ends up less expensive than the Strix Halo boxes, at least here in Ireland. A 128GB Mac Studio with an M5 Max (the Ultra can only have 96 or 256GB) still costs less than the "GMKtec EVO-X2" or the Nvidia DGX Spark with similar performance. Is it the same in the US?
That's not right. Mac Studio M5 Max 128gb, in Apple Store Ireland, starts from 5,909e (~6,847 USD; tax included). On the other hand, GMKtec Evo-X2 128gb can be currently purchased for 3,299e (~3,822 USD; tax included) from GMKtec online store (free shipping from DE warehouse). It's quite the difference in price.
It’s the price I got from amazon.ie
I figured it's dodgy amazon ie listings. DGX Spark/Asus Ascent GX10 prices are inflated there too. They'll also be happy to sell you an RTX 5090 for 10 grand.
That would be such a sizeable disconnect that you might do some arbitrage.
Current prices in USD:
128 GB 1 TB M5 Mac Studio: $5399
128 GB 1 TB GMKtec EVO-X2: $3499
128 GB 1 TB Framework Desktop: $3748
I am really surprised you’re seeing half the generation performance of 3.6 with 3.8 at the same parameter size and quantization (and same prefill performance to boot) - is there just an optimization in the stack somewhere for 3.6 that hasn’t landed yet for 3.8?
Curious if other folks are also surprised by this apparent discrepancy or have a ready explanation.
I suspect this might be due to MTP mispedictions. Either because the 3.8 quantized model weights do not include MTP heads, or as what happened with Ornith-1.5 recently, corrupted MTP heads, or due to a software issue in Ollama.
I remember an article a couple weeks back where someone used an HPE server with two older Xeon E-series CPUs and got reasonable performance by compiling llama with optimal switches for the architecture (memory alignment, page sizes, etc). I was impressed because those Xeons only had AVX2. If you are willing to spend a little more, you can get a slightly newer Xeon with AVX-512 and 4 sockets.
I can't find the post though.
Edit: a quick search from a server refurbisher nearby gives me a dual Xeon Gold 6330 28-core machine with 512GB and a 16GB V100 GPU. The memory is spread across 32 slots, for about £6,470.
the fact that this author cannot get qwen3.8-27b run at the same speed as qwen3.6-27b, says the article is not worth reading. the author does not know anything about how to run local AI. 3.8 and 3.6 are the same model with different weight.
both tg and pp speed are so terrible on author's machine.
Yea Qwen3.8 wasn't fun to use on my 64GB M4 Max either (better than these numbers though), so my new daily driver is Ornith-1.5-35B-A3B-MLX-4bit. I recommend giving that a whirl if you're on similar hardware, it's definitely better than Qwen3.6 35b-a3b which was my go-to before.
https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B
I tried Ornith-1.5-35B-A3B-MLX-4bit on a 32G M2-Pro mac mini with pi and OpenCode. I didn’t get very good results coding in Python, Racket, and TypeScript. I have seen several positive comments like yours so I was probably doing something wrong. I amgetting the new 64G mac mini in 4 weeks, and I made a note to try Ornith-1.5-35B-A3B-MLX-4bit again.
Can you tell more about your experience with Ornith? I've come across it, and the benchmarks on its landing page are unbeleavably good. But it isn't featured on more established benchmarks like ArtificialAnalysis and it's not on OpenRouter, so I wrote it off as scam.
I am running Ornith 1.5 Q4 on my work issued laptop which surprisingly has 40gb of RAM and 20GB of “unified” VRAM.
I am getting 50t/s of prefill and 5-6t/s of inference.
You can’t get crazy with it, but it’s pretty nice to just let it work away at simpler prompts overnight or during lunch break or while I am working on other things.
Being able to literally a 30gb blob and then being able to talk with it while running on your machine it’s a different kind of magic compared to a far away magic in the cloud.
The cheapest card that will run this model very well is a ln unlocked CMP 170HX. But you can run it on a 3090. I run it on an old spare A6000 Ampere. I think I wouldn’t use anything lower than 60 tok/s though, which you can get with MTP etc. I just use a full vllm stack but some people see a lot of speed with ninfer (there are non 5090 ports).
The large RAM Macs are unusable for inference of dense models as of now. Token generation is too slow.
These numbers are a lot lower than I expected from such pricey hardware.
I'll stick with my old AMD datacenter cards thanks. An Instinct MI50 32GB would cost around $550 or so. It has 1TB/s vram bandwidth thanks to its HBM2.
I am also running some old AMD datacenter cards, 2x MI25 in my case. Getting around 30 tokens/second with short context.
Tensor parallel in llama.cpp using RCCL (disabled by default in llama.cpp for some reason). Surprisingly, for these cards HIP is actually faster than Vulkan, unlike the 9070 XT where Vulkan still wins.
ROCm nightlies do actually support these old cards, just not the ROCm stable releases.
any numbers to share? also, what inference engine do you use and with which api?
I use Llama.cpp and OpenAI-style API of course (who doesn't, unless you use Ollama perhaps?)
Ollama sucks, they dropped ROCm support for my card out of the blue, and the Vulkan replacement wasn't ready. The developer just shrugged and didn't care about the people affected so I moved to llama-server. Also, here's some other reasons to avoid ollama: https://sleepingrobots.com/dreams/stop-using-ollama/
And really, ollama are trying to sell their cloud platform and are always running behind llama.cpp in new features like the KV Cache quantisation.
So numbers, I don't really measure it from day to day, I'm not really interested in benchmarks at all. But I just did a quick test, llama 3.1 8b q8_0 does about 70tk/s (with Vulkan up from 45 when I was still running ROCm), and qwen 3.5 9b q8_0 does about 45, I think that includes its time spent thinking, not sure. Overall I'm happy with that.
Smaller sizes and quants are of course faster than this.
It's not terribly much faster than a Mac Studio, but it's one hell of a lot cheaper of course. And for the price of a Mac Studio you can get a lot faster hardware. The only thing where the studio excels is if you need large amounts of vram like 64GB and up and run the models to go with that (I run multiple small ones and spread them over multiple cards)
It's a software issue.
How so? I've had no issues running llama.cpp with vulkan compute on my AMD graphics card (9070 XT). llama.cpp exposes an OpenAI API endpoint, which seems to be the lingua franca for AI applications. Does llama.cpp not work with datacenter AMD graphics cards?
Yes it works great with them through Vulkan. ROCm is more hit and miss, especially because the cards which are affordable (and PCIe, the new ones aren't PCIe compatible!) are already fairly old and have already been dropped by ROCm.
But Vulkan is actually faster too so there is no reason to bother with ROCm anyway.
I don't currently have an actual datacenter card, but I have a Radeon VII Pro, which is basically a 16GB version of the MI50 Instinct with its own fan.
Because OP is running it on an M3 Ultra with Ollama. He'll get much better perf with llama.cpp or MLX, both of which Ollama wraps, albeit very poorly.
Apart from learning, I cant see the point of spending that kind of money and energy to get such awfull token generation speed. Assuming memory bandwith is the bottleneck, is it just a matter of time until we start to see hbm4 based chip able to run qwen3.8 for normal people running at more than 500 token / seconds? Is memory speed the only technological bottlenecks that prevent us from having fast local model?
For me, a HUGE benefit to running local models is that my data stays mine, on my computers only.
Even if Google, OpenAI, Anthropic whatever promise not to use it, how sure can I be of that? Data is gold anyway. And we're in the middle of a massive gold rush. And they've already been caught scraping sites they had no business to, and pirating books. Clearly their promises and the law mean nothing to them. It's just something you pay off in a settlement if you get caught, a cost of doing business.
With local models besides the speed you also lose a lot of inference quality but it helps to mitigate that. For example making sure your RAG inputs are properly prepared and categorised so the model can find them easily without having to wade through a bunch of misdirected crap.
For example what I do with my bookmarks and chats (the latter are recorded per day), is before I enter them into a RAG corpus I run a small LLM over it to summarise what's being discussed or what the webpage is about. That really helped retrieval quality, and doing this is a batch task that can run asynchronously so speed is not very relevant. This way I get a lot closer to SOTA-model retrieval quality (like with Office Copilot 365 looking for conversations in Teams).
PS: I wouldn't be surprised if Microsoft runs something similar on their end :)
But yes the Mac Studio is outrageously priced especially for running such a small model as qwen 27b. For that price you can use something much much cheaper. It only shines for models that are much bigger, because there simply is not much hardware that can address fast 512GB banks.
> my data stays mine, on my computers only.
Which is a regulation constraint on many professions, BTW. Some people simply can't give some data to say, ChatGPT, without comprehensive guarantees written in Sam Altman's blood.
Guarantees that are completely worthless, because these companies will simply pay the fine for breaking their word.
There need to be second order consequences to those who delegate their responsibility to companies that behave like this. They know the contract is worthless, but then proceed to use it as defense for their own gross negligence.
> Guarantees that are completely worthless
They ensure you are not liable when they misuse the information you entrusted them because you took “adequate precautions”.
Yes that's cool for a company but not when I'm the end user, because it's my data that's being misused. It's my problem and having someone to blame doesn't actually solve the problem.
My original point was that, for many professionals, there is a strong incentive to go local with their AI models the same way you wouldn’t be able to use cloud storage without the assurance the data would be physically stored within a specific region.
> For me, a HUGE benefit to running local models is that my data stays mine, on my computers only.
This is a huge motivation for me to build one myself
Sorry I made a typo. When I said:
> For that price you can use something much much cheaper.
I meant "For that model size you can use something much cheaper".
Is there an “easy button” software for macos for making the RAG corpus you mention? Ideally installed from homebrew?
I'm not sure. I don't use Mac anymore. It used to be my daily driver but they pushed me away a few years ago with the constant iOSification. I've heard good thing about LM Studio, that's about it. https://lmstudio.ai/
I just run a server with Linux (previously multiple servers but I found a way to add multiple GPUs to a single one).
I just used Qwen3.8 & Deepseek v4 flash to make it for me.
Similar here, except I used Opus 4.8. I don't mind using cloud models to make code as long as I don't put actual private data into the model. And I can't run qwen 27b reliably at the moment, I need a better gpu.
It was pretty easy mode like this tbh, though you do need to know what you're doing. I use it with llama-server and openwebui.
I guess you can get it a lot more click to go with something like LM Studio though but I want to use it on a server and call upon its services from multiple sources.
The M3 Ultra was a lot cheaper for most of its lifetime, and really the main reason for buying it is if you want a lot of unified RAM, to run bigger models than 27B models.
The new M5 Ultra should deliver ~50% faster token generation (1.2TB/s mem bandwidth); and extrapolating from my M5 Max (since the M5 Ultra is literally just 2x Maxes), probably ~3x faster PP.
But I don't think it's fair to look at this only from monetary ROI vs API. With local models, you get privacy and ownership.
I do not trust _any_ API provider with my most personal information; such as for example, all my messages, emails, daily journals spanning a decade+, all my photos and videos, etc. So it unlocks new use cases that I simply don't feel comfortable with via API.
And a personal assistant with ALL my context and data, locally, has been incredibly useful for me :) Zero outages either, zero "overloaded", etc. Nearly-zero refusals too (I don't run abliterated models; thinking prefill has worked for anything I've wanted to do)
I have 2x 3090s and I get 260 tokens/sec peak (110 avg) with Dflash2 and about 1500t/s prefill with a Q4 quant of Qwen 27b. I didn't buy an overpriced Apple product and it performs much better. It's extremely reliable for Agentic coding and I can run 2-3 simultaneous agents with a full ~260k context window.
I realize the price of NVIDIA has gone up but there are plenty of GPU options from others like AMD and Intel with reasonable performance.
A slow AI tasks that can process my self hosted personal journal, my medical history in fasten, my diet and exercise in Mealie and Sparky Fitness to offer insights once a day or even once a week is better than nothing. Because I would never upload that data to any of the AI companies.
Ornith 1.5 35B MoE is very close in most benchmarks, but runs at 100 tk/s where Qwen3.8 27B runs at 30 tk/s (MBP M5 Max 48GB). It's now my go-to model for quick inquiries. Ornith 1.0 had problems with world knowledge, but 1.5 seems very solid on that front. I'll be running some agentic tests today.
Ornith 1.5 35B is so fast that my CPU doesn't even have time to heat up, where Qwen3.8 would turn my laptop into a toaster within a few minutes. I hope agentic tasks will hold up!
I am also seeing slower speeds, roughly the same ballpark, sometimes even lower - 10-11 tok/s on M5 Max. If there is that ONE version (GGUF or MLX) that runs roughly as fast as 3.6 used to run, please let me know.
What would be incredible is the 3.8 35B MoE version too, I can run 3.6 with 60 tok/s which is a really, really nice speed.
> Realistic minimum RAM 16GB
When someone says stuff like this what does it mean? A Mac/computer with this much RAM capacity/marker (how would this even be useful in this case?) or 16 or ≈16GB free RAM?
Because RAM usage at least on my Mac is another beast altogether and seems nothing will bring down the RAM usage other tham <rm -rf>ing the / (that is if I can and something survives to notice the miracle)
The local LLM scene needs a Draw Things equivalent for Mac. Too much fiddle for things that doesn't make sense (Qwen 3.8 27B should be exactly the same speed as Qwen 3.6 27B). It feels like that I am teasing (I am the author of Draw Things) something, because it is.
I’ve never personally used it, but isn’t that what LM Studio is positioned as? From what I can gather, it seems extremely beginner-friendly.
What you get with Draw Things: 1. Download the app from Mac AppStore; 2. Download the model; 3. Tap "Try recommended settings", have the guarantee that for whatever model it supports, it is the fastest in Mac ecosystem, no need to fiddle.
What you get with local LLM options: 1. Download the inference engine app from the web; 2. Download the model; 3. Configure MTP / DFlash / DSpark whatever; 4. Configure your Pi / OpenCode harness to point to this local LLM inference engine. 5. Configure tools for these harness to be effective. 6. Switching between Ollama, LM Studio, llama.cpp (DwarfStar4), oMLX, MTPLX, to see which one is fastest for your workload. 7. Again switching between different quants of the same model to see which one is less dumb.
To be honest, llama.cpp probably the closest to deliver on "just use it, don't worry about speed" if your focus is about a pure LLM inference engine.
I’ve casually used Draw Things a couple of times (great tool!), and I did not find it beginner friendly. I remember feeling a lot of doubt, confusion, and frustration.
Am I getting these results because I picked the wrong model? Or I need to improve my prompt? Or the tool just can’t do what I’m trying to do? How current are these model recommendations? Have they been superseded by something newer?
As a true beginner to AI at the time, even the sizes and bits were meaningless to me. And I don’t remember having any context as to what I should be attempting to run on my mac.
So I think you need to include steps 4, 5, and 6 of swapping between different models, quants, and prompts. And step 7 is probably wading through the complicated UI, full of jargon that most people don’t know.
Don’t get me wrong, I recognize that it’s a powerful tool, and the steep learning curve exists because it exposes quite a few power-user features. But for someone graduating from commercial AI image generators that take a text prompt and maybe a choice of couple models, it’s not easy.
Yes, I heard you! I think one of the issue Draw Things inherited is the baggage of supporting too much models. Once you settled on a model, then it is just "try recommended settings", and prompt.
The model part is unfortunate, but luckily converging now.
On the LLM side, hopefully it is not an issue too as long as getting aggressive at pruning models.
Or you can just ask Sol to do all that for you, and do split testing, to find best performance.
I would like to see "guarantee" for that app. You get full refound and it is free, right?
Ollama was/is this but they pulled a heel turn...
This low-effort slop post is misleading. It suggest that 3.8 is half as fast as 3.6, but this is almost definitely because MTP isn't enabled by default. The two models should perform the same - they are for me. When you get AI to think for you, you lose.
FWIW, on my Mac Studio I get ~24-27 tok/s generation between 0-16k context in - that's on the Q6_K GGUF with speculative decoding on. I have spent zero effort optimizing/improving this so far but will be trying the 4 bit MLX next (I've tended to find models drop off somewhat below 6 bit but maybe that isn't the case nowadays).
AI is a killer adblocker ! imagine connecting it to instagram and curating all the images that you actually care about removing all ads !
That seems like an expensive way to block ads considering traditional ad blockers have been doing that with a tiny fraction of the compute for decades.
Have you used a social network in the past year? Facebook, YouTube, Pinterest, et al. have become increasingly hostile to blockers with techniques that break pattern-matching on hostnames and element selectors. It may be inevitable that adblockers move toward visual identification.
we keep going to them despite all of this and it just serves to delay any kind of alternative getting pickup (not an easy task, i know)
We go to them because that's where the friends and organizations we care about have chosen to post their content.
Ollama? Running a q1 quant? I don't think the writer knows what the are doing here to be honest.
You will get better information cruising r/localllama for about 10 minutes.
This is a problem that Hugging Face could easily help solve.
Crawling through Reddit or forums to find the right incantation to run a model is frustrating.
There are many, many people who are betraying a lack of facility with these models.
Why is anyone trying to run these on an M3?
I thought it was common knowledge that, if you insist on using Apple, only M5 processors and higher have matrix multiplication units in the cores?
I've seen the same thing with people buying NVidia cards with 16GB of ram and wondering why they aren't getting 100tok/s?
Guys, please, be reasonable. You'll have to get the hardware if you want to run these things fast. If you want to experiment, the slow stuff is fine. But try not to get on HN and ask why your M3 can't get 100tok/s. You're kind of out-ing yourself.
is it really worth to run urself? the watts drsin and that 100% gpu usage
Short answer: no
Long answer: some dude on youtube calculated that even if electricity was free it would take you 37 years to get back your investment.
Slightly longer answer: ... but if you absolutely must have local LLM, a GPU with 24-34 GB VRAM is much cheaper and faster than a Studio
Peaks at over 300 tok/s on my 5090 with Dflash2. At maximum context (252000 or so) still get 60 tok/s. And I don't have n-gram optimization working yet, I only have 64G memory.
I'm running the 5 bit quant on a 3060 12G. Get a handful of tokens/s. Prompts take 10-20 minutes to complete but it works.
>I realized I’d been sitting on the one thing most of those threads were missing: a machine that can actually run it properly, and time to measure it.
This was written by AI I take it. The one thing, huh? All those idiots running it on their RTX 6000s at 140tok/s must be feeling pretty stupid for not getting a Mac Studio instead.
Slop; all those LocalLlama threads "you" read had #s too. And Q1 quant? Why? You have the memory...
> Qwen3.8 27B (Q4_K_M, 17GB) generates at ~14 tokens/s on my Mac Studio M3 Ultra
>~14 tokens/s
For anyone reading that has never ran local llms, please understand that anything under 100 tok/sec is worthless. You are faster typing stuff into Gemini free version that you get with a google account and copy/pasting it in (and you can easily build browser automation with playwright or any other js runtime to have this available in a chat window)
My lower bound is 20 t/s. 100 t/s would be great but even Opus doesn't sustain that.
I'm at ~7.2 t/s on a Shoehorn'd obliterated Qwen 3.8 on my M3 Pro, which is rough.
I get 80 tok/s on the same model, and it's pretty usable. I'm not chatting with it; it's either given a bag of tokens to generate an answer or it's doing some agentic coding.
https://openrouter.ai/anthropic/claude-opus-5 is it worthless because its 65 tps?
re: gemini
https://openrouter.ai/google/gemini-3.7-flash worthless as well?
That being said, 14 tok/s is pretty slow.
The standard is not what you can do with it, the standard is what is the free alternative. Local LLMs need to be able to beat the rate limits on all the free models like Gemini to be useful. The only way to do that is to have high enough tok/sec, especially for agentic loops.
Quality of tokens matters almost as much.
30 is worth it on a meaningfully better model, though annoying. 50 is pretty much unnoticeable (good old 60 FPS). 80-100 is heaven (aka 120 FPS - once you get used to it, it does suck to go back).
I would still rather have 30 tokens per second of GLM 5.3 Flash than 100 tokens per second of Qwen 3.6 35B-A3B.
What? Below 100tk/s is worthless?
I agree that 14t/s is pretty tedious for interactive use, yes. But 50-60tk/s is faster than I can read. 100tk/s is outright fast. Don't forget there is a limit entering content into meatspace.
Also, Gemini may be free but what if I don't want to give all my data to Google? This is precisely why I have a lot of stuff locally.
And will it remain free? How are they going to make back all those trillions of investment?
But yeah I would kinda balk at 14tk/s too that's why I use old datacenter/workstation-class GPUs.
I wouldn't say worthless but once you get used to 100+ tokens / s, it's really a visible slowdown, especially when running multiple agents acting on something more than basic prompt processing.
Still, as you said - for day to day, 50-60 tokens / s is a good baseline.
In agentic scenarios, an LLM has to read far more tokens than it outputs. I think focusing only on the decode speed is somewhat misleading. 14 tok/sec for decode is actually okayish. 93 tok/sec prefill is what's abysmal, my RTX 5090 goes above 2000 tok/sec with 5 bit quants.
I mean, if you don't care about your inputs being trained on, you can just use one of the many free models on openrouter.