OpenClaw Users Weigh P40 Model Quants

The interesting part here is not the GPU itself but the constraint stack: old Pascal silicon, local inference, tool use, and agent reliability all pulling in different directions. On hardware like a P40, the practical answer is usually “smaller, better-tuned models at sensible quants,” not chasing a bigger model that the card can barely serve.

• –`Q4_K_M` is the usual sweet spot for local agent work because it keeps memory pressure down while preserving enough quality for tool calling and instruction following.
• –`Q5_K_M` can be worth it if you can tolerate slower tokens and want a bit more robustness, but the gains are often incremental rather than transformative.
• –For agents, prompt discipline, tool schema quality, and context management usually matter more than squeezing out another quant tier.
• –The post reflects a broader LocalLLaMA pattern: older enterprise cards still work well for 7B-class and some 14B-class models, but reliability depends as much on model tuning as on raw VRAM.