alt Hacker NewsI think especially with the ability for SOTA AI to optimize kernels more people should try their hand at making better inference for their specific hardware.
I have an older W7900 (RDNA3) which, besides 48GB of VRAM, has some pretty decent roofline specs - 123 FP16 TFLOPS/INT8 TOPS, 864 GB/s MBW, but has had notoriously bad support both from AMD (ROCm) as well as llama.cpp.
Recently I decided I'd like to turn the card into a dedicated agentic/coder endpoint and I started tuning a W8A8-INT8 model. Over the course of a few days of autolooping (about 800 iterations using a variety of frontier/SOTA models, Kimi K2.6 did surprisingly well), and I ended up with prefill +20% and decode +50% faster than the best llama.cpp numbers for Qwen3.6 MoE.
I'm currently grinding MTP and DFlash optimization on it, but I've been pretty pleased with the results, and will probably try Gemma 4 next.
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Please share your knowledge and your findings
I think llama.cpp could have done a much better job supporting PC. Sure, some of it us due to bad vendor support but with so many users I am surprised we don't see more optimized inference on standard PCs
In the same boat with 7900xtx. 24GB vram, on paper decent performance, in reality most things don't run. Only llama.cpp is consistent that it can run most models, even if maybe not at top performance (afaik - lacking MTP, problems cache invalidation with hybrid models). At least with llama.cpp I know what runs. With various python-based inferencers, between their uv/venv, my venv, system envs/pythons/libs yadayada - I need an agent to get to the bottom of what's actually running. :-) Yeah IK skill issue/user errors - but don't have seconds in the day left to spend them on that.
Even if not perfect, if you publish on GH or HF, some other agent can maybe start there and not from zero. I did this for Ling-2.6-flash (107B-A7B4 MoE) that's the biggest llm I can ran for practical use on the other h/w I got for local llms (M2 Max). Even if MTP is not working well, still improvement on the current llama.cpp that does not run Ling-2.6-flash at all. This - https://huggingface.co/inclusionAI/Ling-2.6-flash/discussion.... The 4-bit quants are at https://huggingface.co/ljupco/Ling-2.6-flash-GGUF, the branch is at https://github.com/ljubomirj/llama.cpp/tree/LJ-Ling-2.6-flas....