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DeepSeek V4 Pro

最先端
huggingfaceHuggingFace
5.9Mダウンロード4.5KいいねApr 2026公開日1.0M トークンコンテキストMITライセンス100 卓越品質

DeepSeek V4 Pro (1600B parameters) requires approximately 865.4 GB of VRAM with NVFP4 quantization. As a Mixture of Experts model with 49B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 996 GB of VRAM.

はじめに

— コピー&ペーストでローカル実行

Copy-paste commands to run DeepSeek V4 Pro on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "deepseek-ai/DeepSeek-V4-Pro" \ --hf-file "DeepSeek-V4-Pro-NVFP4.gguf" \ -c 4096 -ngl 99

Quick specs

Parameters1600B (49B active)
Architecturemoe (MoE)
Context1.0M tokens
Modalitytext
Min RAM624 GB
Rec. RAM896 GB (NVFP4)
LicenseMIT
FamilyDeepSeek
✓ Code✓ Reasoning

About this model

DeepSeek V4 Pro is a 1.6T-parameter sparse MoE (49B active, 384 routed + 1 shared expert) built for million-token agentic reasoning. Experts ship natively in FP4, so the real on-disk footprint is roughly 862 GB (FP4 experts + FP8 attention) rather than the trillion-scale FP16 size — but it is still a server/workstation deployment: realistic local use targets 8x 80GB GPUs or 1 TB+ unified memory, and at long Think Max contexts the KV cache dominates.

  • •1.6T total / 49B active sparse MoE — 384 routed + 1 shared expert
  • •Native FP4 experts: ~862 GB on disk, not trillion-scale FP16
  • •1M-token context for million-token agent workflows
  • •Server/workstation class — use distills or the Flash variant for local

関連モデル

あなたのハードウェア

検出中...

量子化オプション

量子化レベル別VRAM推定値

No hardware detected — fit column shows raw VRAM estimates

QuantBitsVRAMQualityFit
Q2_K
2
624.0 GB
Low—
Q3_K_S
3
784.0 GB
Low—
NVFP4
4
896.0 GB
Medium—
Q4_K_M
4
976.0 GB
Medium—
Q5_K_M
5
1152.0 GB
High—
Q6_K
6
1312.0 GB
High—
Q8_0
8
1712.0 GB
Very High—
F16
16
3280.0 GB
Maximum—

Quality benchmarks

DeepSeek V4 Pro benchmark scores

Benchmark verified

Coding

SWE-bench Verified80.6%
HumanEval+—
Aider Polyglot—
LiveCodeBench93.5%

Reasoning

MMLU-Pro87.5%
GPQA Diamond—
MATH-500—
ARC Challenge—

Source: vendor-reported · 2026-04-24

ハードウェア互換性

全ハードウェアの適合度推定

カリキュレーターを開く

Computing compatibility...

メモリ内訳

Reference: RTX 2060 6GB

Weights862.0 GB
KV Cache1.9 GB
Runtime0.9 GB
Headroom0.6 GB

よくある質問

FAQ — DeepSeek V4 Pro

関連項目

量子化ガイドスコアリングメソドロジーカリキュレーターを開く