KV cache, not weights, limits LLM server user capacity

The KV cache, not the model weights, is the primary determinant of how many users an LLM server can support. While model weights for a LLaMA-2 7B model occupy 13.5 GB, the KV cache for a single 4,096-token sequence can consume 0.5 MiB, rapidly escalating with longer contexts and more users. Techniques like multi-query attention, grouped-query attention, cache quantization, sliding windows, and PagedAttention are employed to reduce the KV cache's memory footprint, thereby increasing server throughput. AI

IMPACT Optimizing KV cache usage is crucial for scaling LLM inference and reducing serving costs.

RANK_REASON Technical explanation of LLM inference optimization techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• graphics processing unit
• Grouped Query Attention
• KV cache
• LLaMA-2 70B
• LLaMA-2 7B
• mistral:7b
• multi-query attention
• PagedAttention

• infra

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