How far are we from a local model being able to optimize it's own runtime stack?
So given that open source is about 4-6 months behind, can we reasonably expect that local runnable models will start having the ability to dissect a big repo like llama cpp and optimize it for a given set of hardware sometime starting next year?
Proof1 community thread
Key points
- A lot of projects have come up recently consisting of specific hardware config specialized forks of existing conferencing engines, but they all seem to be made using Claude or OpenAI's biggest frontier models.
- Or maybe are models like deepseek4.1 or glm5.3 already able to do that?
- Maybe even qwen flash next is capable of doing this?
- Qwen 4 is coming out end of October, anyone expect that model to be able to pull self-optimization off?
Sources (1)
- [1]How far are we from a local model being able to optimize it's own runtime stack?r/LocalLLaMA (top, daily) · Oct 13, 01:01 AM
So given that open source is about 4-6 months behind, can we reasonably expect that local runnable models will start having the ability to dissect a big repo like llama cpp and optimize it for a given set of hardware sometime starting next year?
A lot of projects have come up recently consisting of specific hardware config specialized forks of existing conferencing engines, but they all seem to be made using Claude or OpenAI's biggest frontier models.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 12, 2026Qwen 3.8 Flash Next Q3 S - Benchmark creation
- Oct 12, 2026Qwen3.8-27B vs Frontier vs Others
- Oct 12, 2026[AINews] Meta Muse Wonder & Wonder Full: MSL’s Claude 5.5-tier Frontier Model
- Oct 12, 2026Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB
- Oct 12, 2026AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
- Oct 11, 2026I ran Qwen 3.8 Flash-Next on my AMD 7900 XTX at 500k context. All local and what a shift it has been.