Unverified50% confidenceFactExact time
After loading a 70B-parameter model onto an 80GB A100, the space left for the KV cache is extremely limited, and when concurrency increases or context length grows, the KV cache can rapidly exhaust VRAM, triggering OOM and crashing the inference process.
1
Sources
50%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
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Related Claims
VerifiedFor a model with 70B parameters, the KV Cache for an 8K context may occupy several additional gigabytes of VRAM76% 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.76% similarVerifiedThe 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 H100.75% similarVerifiedA 70B parameter model quantized with Q4_K_M occupies approximately 40GB of storage, and can achieve an inference speed of 10–20 tokens per second on an RTX 4090 with CPU memory offloading.73% similarUnverifiedFor 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/H100.73% similar
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