Unverified60% confidenceFactExact time
For 1 million tokens, the KV Cache alone can consume hundreds of GB of VRAM at FP16 precision, far exceeding the 80 GB capacity limit of a single A100/H100.
2
Sources
60%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
Related Claims
VerifiedThe KV Cache of a 70B-parameter model with 128K context support can require hundreds of gigabytes of VRAM at full load, exceeding the 80GB VRAM capacity of a single H100.85% similarVerifiedTaking LLaMA-2 70B as an example, when processing a 4096-token context with a batch size of 32, the KV Cache can consume over 80 GB of memory.82% similarUnverifiedEvery 1,000 in-flight tokens require about 0.33 GB of VRAM in the KV cache.79% similarUnverifiedIf all 36 layers of Spark X 2.5 tracked the full text, the KV cache overhead for tracking 1 million tokens would be approximately 155 GB, while the sparse design compresses this to approximately 39 GB76% similarVerifiedFor a typical 7B model with 32 layers and 32 attention heads, storing the KV Cache for each token requires approximately 0.5 MB of VRAM, so generating 1,000 tokens requires an additional ~500 MB of VRAM76% similar
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