91–100 of 178 posts
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Yeah 27B is way too slow for the Strix Halo. Laguna was better but still slow when I tried it. Qwen3.6 35B is still the best today.
Have you given Ornith-1.5-35B a shot? It's been a pretty decent step up for me compared to Qwen3.6 https://news.ycombinator.com/item?id=49362401
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The best solution to this for me is to self host litellm or a different router and use model aliases. For example I have a model called "coding" and when a new good model comes out I just switch the backend without needing to change the alias or the key in my projects (opencode, etc). I have a few of them even a smart router called "agents" which will use local models but if it thinks the request might require higher…
I built a router that lets you route between local and cloud models. Link in my profile.
if (process.env.LOCAL_MODEL {
http('localhost:3000/v1/completions')...
} else {
http('api.openrouter.ai/v1/completions')...
}Finally, a reason to own a 128GB Strix Halo or GB10 device. Or a reason to consider the new Mac Studio. I have a Strix Halo and dual 32GB GPUs in my desktop, and the latter is pretty much always better for running local models because it's quite a bit faster due to higher memory bandwidth. There simply haven't been any models that are better than Qwen 27B or Gemma 31B, which run comfortably in 64GB with big context.…
The 3080ti is 912.4 GB/s
Really looking forward to this, 27B is a struggle with a strix halo and Laguna 2.1 can do stupid things for tooling calls.
It will be interesting to see the intersection of this with inference engines like FreeToken which improve distribution of work for MoE models across CPU/RAM and GPU/VRAM. If all it takes for a competitive model to run locally at good speeds is a used 3090 and some DDR4, then we might be in for the year of local AI. https://github.com/FlashML-org/FreeToken
Have you tried FreeToken yourself? I was hoping to find some benchmarks on their github but took a quick pass at their research paper and it seems they're showing ~2x performance on qwen 3.6 35b when compared to llama.cpp - but llama.cpp is so sprawling and has so many options I find that a difficult comparison.
Yeah I'm sometimes unsure how to get best perf out of llama.cpp, and honestly thought it already did what the FreeToken paper discusses, but from everything I've been able to find since llama.cpp has no dynamic expert cache for GPU. An RFC discusses adding such capability and there's impressive results some are claiming from a fork, but I had to stop reading the thread, reading all the LLM generated comments and summaries from people was making me dizzy.
RFC here https://github.com/ggml-org/llama.cpp/discussions/24528 which also links to some experimental implementations throughout the thread.
Finally, a reason to own a 128GB Strix Halo or GB10 device. Or a reason to consider the new Mac Studio. I have a Strix Halo and dual 32GB GPUs in my desktop, and the latter is pretty much always better for running local models because it's quite a bit faster due to higher memory bandwidth. There simply haven't been any models that are better than Qwen 27B or Gemma 31B, which run comfortably in 64GB with big context.…
A 3060ti 8gb, released in 2020, has 448 GB/s of bandwidth compared to the Halo 256 GB/s The 3080ti is 912.4 GB/s
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What sort of pp/tg speed do you get on a Strix Halo?
This is the best I got, all with Unsloth's quantizations. Laguna-S-2.1:UD-Q4_K_XL (no MTP) pp=186.4 t/s tg=27.8 t/s Qwen3.6-35B:UD-Q4_K_XL (with MTP) pp=404.4 t/s tg=83.2 t/s Qwen3.6-27B:UD-Q4_K_XL (recorded pre-MTP) pp=343 t/s tg=12.1 t/s Laguna actually performed better than I remembered. I thought it was slower.