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DeepSeek-V4-Pro-NVFP4 GPU Requirements: VRAM & Cheapest GPU

DeepSeek-V4-Pro-NVFP4 has about 910B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.

910BParameters
496 GBMin VRAM
$6.56/hrCheapest
< 2 minDeploy
nvidia/DeepSeek-V4-Pro-NVFP4
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910B paramstext-generationdeepseek_v45.9K downloads51 likesupdated May 29, 2026

To run DeepSeek-V4-Pro-NVFP4 for inference at FP16, you need roughly 1984 GB of VRAM. The cheapest fit on Spheron is 8x B300 288GB at about $83.52/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.

GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k

Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.

RANKCONFIGURATIONPER GPUTOTAL $/HR
  • 01
    8× B300 288GBCHEAPEST
    Blackwell Ultra · HBM3e
    $10.44/hr$83.52/hr

Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.

VRAM required to run DeepSeek-V4-Pro-NVFP4

Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.

PrecisionInferenceLoRA fine-tuneFull fine-tune
FP161984 GB2976 GB7935 GB
INT8992 GB1488 GB3967 GB
INT4496 GB744 GB1984 GB

Cheapest GPU to run DeepSeek-V4-Pro-NVFP4 by precision

FP16
VRAM required1984GB

Full precision. Best quality, highest memory.

Cheapest GPU
8x B300 288GB
Blackwell Ultra · HBM3e
$83.52/hr · $10.44/hr/gpu
8x B300 288GB on Spheron
INT8
VRAM required992GB

8-bit quantized. ~2x smaller, minimal quality loss.

Cheapest GPU
8x H200 141GB
Hopper · HBM3e
$26.48/hr · $3.31/hr/gpu
8x H200 141GB on Spheron
INT4
VRAM required496GB

4-bit quantized. ~4x smaller, runs on smaller GPUs.

Cheapest GPU
8x A100 80GB
Ampere · HBM2e
$6.56/hr · $0.82/hr/gpu
8x A100 80GB on Spheron

Inference vs fine-tuning DeepSeek-V4-Pro-NVFP4

InferenceWeights + KV cache
LoRA fine-tune~1.5×+ low-rank adapter
Full fine-tune~4×+ gradients + optimizer state

Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For DeepSeek-V4-Pro-NVFP4, an on-demand B300 288GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.

Deployment guideDeploy DeepSeek V4 step by stepHands-on production setup, GPU configs, and benchmarks for DeepSeek-V4-Pro-NVFP4.Read guide

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