Unverified60% confidenceBenchmarkExact time
Llama 3 8B has 32 layers, 8 KV heads (GQA architecture with 32 query heads), a head dimension of 128, and a KV cache of approximately 1.07 GB for a single sequence under FP16 with an 8192 context length.
2
Sources
60%
Confidence
Long-term
Relevance
8/12/2026
First Seen
Sources
Related Claims
Verified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83GB76% similarVerifiedA single sequence of 32K context length for LLaMA-2 70B can require approximately 80 GB of KV cache in FP16.75% similarUnverifiedThe pve-01 node is configured with a Ryzen 7 5700U (8 cores, 16 threads), 62 GB of RAM, 512 GB NVMe (ZFS) + 2 TB HDD (ZFS) + 256 GB SATA SSD, and serves as the primary host for the Kubernetes cluster.72% similarUnverifiedFor 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.71% similarUnverifiedUnder the same conditions of Llama-3.1-8B, batch size 32, and context 2048, vLLM achieves about 450 tok/s, llama.cpp about 280 tok/s, and Ollama about 120 tok/s.71% similar
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