KV Cache Quantization Expands Qwen 35B Context 8x on a 12GB GPU

A developer running Qwen 35B on an RTX 4070 with 12GB VRAM faced a critical bottleneck: with 95% of VRAM already in use, expanding the context window from 4,096 to 32,768 tokens was impossible without running out of memory. The key insight was that the KV cache — which stores all previously processed tokens — grows linearly with context length, making it the primary target for optimization. By switching the Key and Value cache data types from the default 16-bit (f16) to 8-bit quantization (q8_0) using llama.cpp flags, the cache's VRAM footprint was cut by roughly 50%. Benchmarks showed the 8x context expansion came with negligible quality loss and no measurable speed reduction, with community data suggesting under 0.1% perplexity degradation for q8_0. The developer recommends this approach specifically for heavy agent workloads, where tool definitions and system prompts can easily consume 19,000 tokens in a single turn.