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

DeepSeek-V4-Pro-DSpark has about 889B 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.

889BParameters
485 GBMin VRAM
$6.56/hrCheapest
< 2 minDeploy
deepseek-ai/DeepSeek-V4-Pro-DSpark
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889B paramstext-generationdeepseek_v45.5K downloads210 likesupdated Jun 27, 2026

To run DeepSeek-V4-Pro-DSpark for inference at FP16, you need roughly 1939 GB of VRAM. The cheapest fit on Spheron is 8x B300 288GB at about $28.00/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
    $3.50/hr$28.00/hr

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

VRAM required to run DeepSeek-V4-Pro-DSpark

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
FP161939 GB2909 GB7756 GB
INT8970 GB1454 GB3878 GB
INT4485 GB727 GB1939 GB

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

FP16
VRAM required1939GB

Full precision. Best quality, highest memory.

Cheapest GPU
8x B300 288GB
Blackwell Ultra · HBM3e
$28.00/hr · $3.50/hr/gpu
8x B300 288GB on Spheron
INT8
VRAM required970GB

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

Cheapest GPU
4x B300 288GB
Blackwell Ultra · HBM3e
$14.00/hr · $3.50/hr/gpu
4x B300 288GB on Spheron
INT4
VRAM required485GB

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-DSpark

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-DSpark, 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-DSpark.Read guide

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DeepSeek-V4-Pro-DSpark GPU questions