Running 1M Context Window on 24GB VRAM Using KVarN Quantization
A user successfully ran a 1-million token context window on a single 24GB RTX 3090 GPU using a 35B model (Qwen 3.5 35B A3B). This was achieved by implementing 4-bit Variance-Normalized KV-Cache Quantization (KVarN) in a custom fork of llama.cpp called BeeLlama.cpp. This milestone demonstrates that consumer-grade hardware can handle massive context lengths without losing retrieval accuracy, making long-context LLM applications much more accessible. It highlights KVarN as a superior alternative to standard 4-bit KV cache quantization, which typically suffers from severe precision loss at long contexts. The 35B model occupied 17 GB of VRAM, leaving limited space for the KV cache, which was compressed using KVarN. The setup successfully passed a "needle-in-a-haystack" test by retrieving 7 distinct pieces of information placed throughout the 1-million-token text.
## BACKGROUND
KV-Cache (Key-Value Cache) stores past key-value states to avoid redundant computations during LLM generation, but it grows linearly with context length and consumes massive VRAM. KVarN (Variance-Normalized KV-Cache Quantization) is a technique developed by Huawei that applies a Hadamard rotation and dual-scaling variance normalization to compress the cache to 4-bit while mitigating error accumulation. The "Needle in a Haystack" test evaluates an LLM's ability to retrieve specific, targeted information embedded deep within a large body of text.