Topic
Gemma
2 episodes
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My 8GB GPU shouldn't run flagship local LLMs, but this workflow makes it work anyway
The episode explores how an 8GB GPU—well below today’s flagship local LLM hardware—can still deliver useful local AI with careful model selection, quantization, and load tuning. The hosts dig into the article’s real-world workflow: mixing medium and tiny models (Qwen 3.5 9B, Gemma 4 E4B, LFM2.5 1.2B) at specific quant levels, using GPU offload, and matching each model to the right job. They debate whether these clever workarounds change anything practical for most users, and where the technical limits still bite.
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Introducing OpenRL: A self Hosted post training API for fine tuning LLMs | Google Open Source Blog
Justy and Cody discuss Google’s OpenRL, a self-hosted post-training API that tries to separate RL research loops from the Kubernetes and GPU infrastructure underneath them.