Public frontier models lean progressive on open benchmarks. Local inference via llama.cpp is still the practical way to keep an uncensored second opinion. Memory is the bottleneck; TurboQuant-style KV compression is what will make local writing workers routine in multi-model agents.
OpenRouter put numbers on a pattern agent builders already use: a panel of models with a judge synthesizer beats the best single model, and a budget panel matched frontier quality at half the cost. What that means for your AI platform.
A small coding model mocked on r/LocalLLM is actually a clean case of execution-verified distillation. Here is how the method works, when distilling a frontier model into a cheap local one pays off, and the three things the hype leaves out.
Most AI infrastructure teams spend 35-60% more on GPU compute than they need to. The cause isn’t cloud pricing. It is architecture, and it is fixable.
When Tesla published patent WO2024073080 describing a new file format internally called “.smol”, the headline was simple: 4x reduction in IOPS for AI training. Most people read this as a hardware story. It isn’t. It’s a data architecture story. And it exposes a structural weakness in how most enterprise data Read more
GPUs are no longer the bottleneck. Data movement is. I recently attended an online talk that stayed with me longer than most.Not because of a new GPU announcement, but because it clearly articulated something I have seen repeatedly over the years, across very different systems. That message, strongly emphasized by Read more