KV Cache Memory Explained: Estimating and Reducing VRAM Usage in LLMs

The KV cache, a critical component for LLM inference, can consume significant VRAM, often exceeding the memory required for model weights, especially at longer context lengths or higher batch sizes. A simple formula can estimate KV cache memory: 2 × layers × hidden_dim × context_length × bytes_per_param. For instance, Llama 3.1 70B at 128K context requires 340GB for its KV cache. Architectural improvements like Multi-Query Attention (MQA) or Grouped Query Attention (GQA) are highly effective, reducing cache memory by 4-8x by sharing Key and Value matrices. Quantization to FP8 or INT4 and techniques like Sliding Window Attention or vLLM's PagedAttention also help manage KV cache memory, though with varying impacts on quality and use cases. AI

IMPACT Provides practical guidance for optimizing LLM inference hardware usage and avoiding VRAM limitations.

RANK_REASON The item provides practical advice and formulas for managing LLM inference hardware resources.

Read on dev.to — LLM tag →

• A100
• Fp8
• GQA
• Grouped Query Attention
• Int4
• KV cache
• Llama 3.1 70B
• multi-query attention
• speculative decoding

• infra

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