Unverified50% confidenceFactExact time
AirLLM 能够在单张仅有4GB显存的GPU上完成70B大模型的推理
1
Sources
50%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
VerifiedTaking 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.69% similarVerifiedA GPT-4-class model with 175 billion parameters requires approximately 175 GB of VRAM even when stored at fp8 precision.68% similarUnverifiedUsing GGUF Q4 quantization as a baseline, a 7B model requires approximately 4–6 GB of VRAM, a 13B model requires approximately 8–10 GB, and a 70B model requires more than 40 GB or multi-GPU parallelism.68% similarUnverifiedA single A100 (80 GB VRAM) can run 7B–13B scale models, while 4× A100s (320 GB VRAM) can run 70B scale models.68% similar
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