Multi-agent systems can consume many tokens when agents share unrestricted context, leading to higher token usage than necessary. MARL's centralized training with decentralized execution pattern gives you the blueprint: design coordination with full visibility, then run each agent with local…

Two formats, two bets about which weights matter. GPTQ compresses a trained model to 4 bits in a single pass. It quantizes one weight column at a time, then uses second-order sensitivity data to spread that rounding error across the weights it hasn't touched yet. AWQ goes another way. It finds the…

You can't pick how to fine-tune an LLM based on vague rules of thumb. The decision hinges on compute limits, task type, and how much accuracy you're willing to trade. Full fine-tuning updates every parameter in the model, giving you maximum task performance but requiring substantial GPU memory for…