alt Hacker NewsRun Qwen3.8 27B locally: real numbers from my Mac Studio
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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.
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.
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.
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.
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.
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.
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.
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.
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).
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?
AI is a killer adblocker ! imagine connecting it to instagram and curating all the images that you actually care about removing all ads !
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.
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 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.
is it really worth to run urself? the watts drsin and that 100% gpu usage
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.
Slop; all those LocalLlama threads "you" read had #s too. And Q1 quant? Why? You have the memory...
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> 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'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?