Expired60% confidenceFactExact time
Deploying an open-source model with 70 billion parameters running at FP16 precision requires at least four A100/H100 GPUs with 80 GB of VRAM each, with hardware procurement costs alone exceeding one million yuan.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/6/2026
First Seen
Valid until: 10/4/2026(expired)
Sources
Related Claims
UnverifiedRunning an open-source model at the 70-billion-parameter scale requires at least approximately 140GB of VRAM in FP16 precision, i.e., 8 H100s in parallel.80% 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.76% similarVerifiedA GPT-4-class model with 175 billion parameters requires approximately 175 GB of VRAM even when stored at fp8 precision.75% similarUnverified英伟达H100 GPU单卡功耗高达700瓦，一个配备5万块H100的超算集群总功耗可达35兆瓦（MW）74% similarVerifiedConsumer-grade GPUs such as the RTX 4090 have 24 GB of VRAM and can run quantized models with billions of parameters.74% similar
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