Verified80% confidenceFactExact time
以LLaMA-3 70B（80层、64头、每头128维）为例，单个token的KV Cache约占2.6MB（FP16精度），32K上下文的KV Cache总量约达83GB
7
Sources
80%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
In-Memory Layer Mapping: How to Effectively Solve LLM Context Overload
hackernewshackernews7/4/2026
Related Claims
UnverifiedFor a typical 7B model (32 layers, 32 heads, 128 dimensions) processing a 2048-token sequence at FP16 precision with a batch size of 8, the KV Cache consumes approximately 4 GB of VRAM.83% similarUnverifiedA single sequence of 32K context length for LLaMA-2 70B can require approximately 80 GB of KV cache in FP16.82% similarUnverifiedWhen using FP16 precision, the KV Cache for LLaMA-2-7B occupies approximately 0.5 MB per token.82% similarVerifiedTaking a 70B parameter model with FP16 precision and 32 attention head layers as an example, the KV Cache for a single 200K-length request requires approximately 80–120 GB of VRAM, far exceeding the 80 GB VRAM limit of a single A100 GPU.79% similarUnverifiedCalculated from a storage perspective: 27B parameters × 2 bytes (FP16) ≈ 54 GB, INT8 ≈ 27 GB, INT4 ≈ 13.5 GB, 2-bit ≈ 6.75 GB, 1.5-bit approaches 5 GB; fitting within 4 GB means the average bit width needs to be below 1.5-bit.77% similar
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