❌Won't fitfp16 · 4K ctx · batch 1

Does H100 run DeepSeek-V3 671B? No at fp16.

At fp16 precision, DeepSeek-V3 671B (671 B MoE parameters) needs roughly 1347.0 GB of VRAM. H100 only has 80 GB, short by 1267.0 GB. INT4 quantization brings it down to 343.2 GB.

>999%
VRAM
Won't fit
Estimated VRAM (fp16)
1347.0 GB
of 80 GB on H100· 1267.0 GB short
Tok/sec
38
TFTT
900ms
Power
490W
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Memory breakdown (fp16)

Quantization comparison

Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.

PrecisionVRAMUtilisationFits on H100?
FP16 (full precision)1347.0 GB1684%❌Won't fit
INT8 (8-bit)677.8 GB847%❌Won't fit
INT4 / Q4 (4-bit)343.2 GB429%❌Won't fit

Frequently asked

Can H100 run DeepSeek-V3 671B?
Not at fp16 — DeepSeek-V3 671B needs about 1347.0 GB while H100 has 80 GB. It fits at INT4 quantization (343.2 GB) with some accuracy trade-off.
How much VRAM does DeepSeek-V3 671B use?
About 1347.0 GB at fp16, 343.2 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
What's the fastest way to run DeepSeek-V3 671B on H100?
Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For H100, enable Flash Attention 2.
Where can I rent a H100?
GPUniq aggregates live H100 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.

Other GPUs for DeepSeek-V3 671B

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None of the common quantization levels fit this model on H100. Consider multi-GPU deployment or a larger card.