KVarN quantization benchmarks demonstrate that 6-bit KV cache compression achieves quality parity with q8_0 quantization, while 4-bit variants match q5_0 performance. This represents a 33-50% reduction in KV cache memory footprint compared to standard approaches. For operators running inference on constrained hardware, this directly extends context window capacity without architectural changes. A system previously limited to 8K tokens on 16GB memory could plausibly extend to 12-16K tokens using 4-bit KV quantization. Cost per inference token decreases proportionally when context length expands on fixed hardware. Builders optimizing for edge deployment or multi-user batch inference face a new decision: accept minimal quality degradation to reduce memory pressure, or maintain unquantized KV caches at higher operational cost. This shifts the optimization frontier downward—previously, 4-bit KV quantization triggered noticeable degradation; KVarN now makes it viable as a standard compression layer. Quantization selection becomes a tunable knob in the inference pipeline rather than a binary choice.