Llama 3.1 8B Feels Slow Locally

This smells like an inference-stack problem, not an inherent Llama 3.1 8B problem. Meta and AWS both frame the 8B model as suitable for local or edge deployment, so “minutes per prompt” usually means CPU fallback, poor quantization, or bad offload settings.

• –64GB RAM helps with capacity, but throughput is driven by GPU offload, VRAM, and runtime configuration.
• –If the backend is spilling onto CPU, an 8B model can still feel painfully slow.
• –Quantization quality matters a lot; a weak or heavy quant can erase the benefit of a decent GPU.
• –Long prompts and large context windows add latency fast, so chat history bloat can make “simple” tasks look slow.
• –The right fix is to benchmark one known-good local stack, then change one variable at a time.