KV Cache Quantization Levels Benchmarked on SWE-bench: Practical Trade-offs for Local Inference

Systematic benchmarking work on KV cache quantization levels provides crucial empirical guidance for practitioners optimizing memory-constrained local deployments. Using the practical SWE-bench-lite benchmark (which emphasizes coding and reasoning tasks), researchers are collecting real-world performance data across quantization levels—moving beyond theoretical analysis to show actual quality trade-offs. The live dashboard and repository track these results as the study expands.

This work addresses a critical gap in local inference optimization: while attention and weight quantization are well-studied, KV cache quantization remains empirically under-explored relative to its memory impact. For single and dual-GPU users running context-heavy workloads, KV cache memory can become the limiting factor before weights do. Data-driven benchmarks showing which quantization levels preserve reasoning quality while saving memory directly inform deployment decisions and enable users to squeeze longer contexts and higher throughput from fixed hardware budgets.

Source: r/LocalLLaMA · Relevance: 8/10