Ollama? Running a q1 quant? I don't think the writer knows what the are doing here to be honest.<p>You will get better information cruising r/localllama for about 10 minutes.
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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.<p>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.
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.<p>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.
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.
Well, yes and no.<p>The author has an M3.<p>Here's reality, MLX on the software layer will not magically place hardware matrix multiplication units in your GPU cores.<p>Newer Macs are always just gonna smoke anything earlier than an M5.<p>Not even sure why this person is trying to get this stuff to run on hardware that wasn't designed for AI?<p>Everyone is pointing him to newer hardware precisely because you <i>need</i> 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.
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?
These numbers are a lot lower than I expected from such pricey hardware.<p>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.<p>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.<p>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?
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 for 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 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?<p>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.<p>I can't find the post though.<p>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 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.
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.<p>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.<p>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.<p>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).<p>PS: I wouldn't be surprised if Microsoft runs something similar on their end :)<p>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.<p>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.
Is there an “easy button” software for macos for making the RAG corpus you mention? Ideally installed from homebrew?
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.<p>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.<p>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.<p>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.<p>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)
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. When you get AI to think for you, you lose.
> Qwen3.8 27B (Q4_K_M, 17GB) generates at ~14 tokens/s on my Mac Studio M3 Ultra<p>>~14 tokens/s<p>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 mean, if you don't care about your inputs being trained on, you can just use one of the many free models on openrouter.
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.<p><a href="https://openrouter.ai/anthropic/claude-opus-5" rel="nofollow">https://openrouter.ai/anthropic/claude-opus-5</a> is it worthless because its 65 tps?<p>re: gemini<p><a href="https://openrouter.ai/google/gemini-3.7-flash" rel="nofollow">https://openrouter.ai/google/gemini-3.7-flash</a> worthless as well?<p>That being said, 14 tok/s is pretty slow.
What? Below 100tk/s is worthless?<p>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.<p>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.<p>And will it remain free? How are they going to make back all those trillions of investment?<p>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.<p>Still, as you said - for day to day, 50-60 tokens / s and my interactions is generally fine.