VQV Signal
Long-context LLM inference faces memory bottlenecks due to the key-value (KV) cache. Tailored vector quantization methods are proposed to enable aggressive 1-bit KV cache compression while addressing degradation issues in existing approaches.
Long-context LLM inference faces memory bottlenecks due to the key-value (KV) cache. Tailored vector quantization methods are proposed to enable aggressive 1-bit KV cache compression while addressing degradation issues in existing approaches.
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The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidth. To mitigate this bottleneck, vector quantization (VQ) has emerged as a promising approach for aggressive KV cache compression. However, existing VQ methods...
Reducing the KV cache memory footprint is critical for scaling LLMs to longer contexts without overwhelming hardware resources. Improved 1-bit quantization techniques can make large-context LLM inference more efficient and feasible.
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