I benchmarked 13 models at 65K-128K context to find out what actually matters for agentic workloads

Summary

I benchmarked 13 models at 65K-128K context to find out what actually matters for agentic workloads — prefill dominates everything, and KV head count beats parameter count I've been running local LLMs for agentic workflows (tool use, coding agents, RAG) and kept seeing people obsess over tg128 (token generation speed) as the headline performance metric. So I ran a structured long-context benchmark to figure out what actually matters when your context window is full. The answer surprised me.

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• Concurrency plus nvfp4 on Blackwell
• 5060 worth it?
• Getting close to 100K context on 32GB VRAM with Qwen3.6-27 at Q8
• PSA: Upscaling Gemma 4 requires a proportional layer_scalar adjustment
• Using "applications" to make a smaller model more effective at bigger tasks.
• Appreciation post!