DeepSeek-V4-Pro-NVFP4
DeepSeek-V4-Pro-NVFP4 is a quantized Mixture-of-Experts language model with 1.6 trillion total parameters (49 billion active). NVIDIA has optimized it using their Model Optimizer toolchain, compressing weights and activations to NVFP4 format for efficient inference. It supports up to 1 million token context, multiple reasoning modes, tool calling, and structured output. Licensed under MIT, it is ready for both commercial and non-commercial deployment on NVIDIA Blackwell GPUs.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 910B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 154k |
| Likes | 70 |
| Last updated | 2026-06-14 |
| Source | nvidia/DeepSeek-V4-Pro-NVFP4 |
What DeepSeek-V4-Pro-NVFP4 is
The model is a post-training quantized derivative of DeepSeek-V4-Pro, using NVFP4 (8-bit floating-point) for weights and activations in linear layers within MoE transformer blocks. Evaluated on GPQA Diamond, AA-LCR, τ²-Bench Telecom, SciCode, and IFBench benchmarks; performance comparable to FP8 baselines. Requires tensor parallelism (4–8 GPUs tested on GB300/Blackwell B200). Integration verified with vLLM (0.22.1rc1+) and SGLang (PR #25820+). Input: text with multi-turn conversation support and custom encoding (encoding_dsv4). Output: text with function-calling and reasoning content. Trained on undisclosed base datasets; calibrated on cnn_dailymail and Nemotron-Post-Training-Dataset-v2.
Run DeepSeek-V4-Pro-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/DeepSeek-V4-Pro-NVFP4")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
NVIDIA Blackwell GPUs (B200 verified; tensor parallelism 4–8+ required). Tested on GB300 with vLLM 0.22.1rc1. VRAM estimate: ~450–550 GB total (quantized weights ~115 GB per copy; KV cache and optimizer state require significant additional memory per tensor-parallel rank). Linux OS required. FP8 weights and activations in MoE layers.
Unknown. Model card does not disclose LoRA, QLoRA, or full fine-tuning feasibility post-quantization. NVFP4 quantization may constrain gradient updates in linear layers. Recommend contacting NVIDIA or DeepSeek teams for fine-tuning guidance; base DeepSeek-V4-Pro resources may provide insight.
When to avoid it — and what to weigh
- Real-Time or Latency-Critical Applications — Model requires tensor parallelism (4–8+ GPUs). Single-GPU inference not documented; appropriate for batch processing and offline tasks, not sub-second response requirements.
- Multi-Language or Non-English-Centric Use Cases — Base model training data and calibration datasets (cnn_dailymail, Nemotron v2) are English-dominant. Non-English performance undocumented; not recommended for multilingual or non-English primary deployments.
- Edge Deployment or Resource-Constrained Environments — 909B parameters (even quantized to FP4) require high-end NVIDIA infrastructure. Not suitable for mobile, embedded, or on-device inference. Distributed GPU clusters (Blackwell or newer) mandatory.
- Safety-Critical or Highly Regulated Domains Without Guardrails — Model card explicitly notes toxic language, societal biases from training data, and potential for inaccurate or socially unacceptable output. Requires developer-implemented guardrails, safety mechanisms, and domain-specific validation before regulated industry deployment.
License & commercial use
Licensed under MIT (Open Source Initiative-approved permissive license). See https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/LICENSE for full terms. NVIDIA states the model is a third-party quantized derivative; underlying DeepSeek-V4-Pro is owned by DeepSeek.
MIT is an OSI-compliant permissive license permitting commercial use, modification, and distribution with appropriate attribution and license inclusion. Model card explicitly states 'ready for commercial/non-commercial use.' However, developers remain responsible for model inputs/outputs, guardrails, and compliance with use-case-specific regulations and safety standards. No warranty or liability indemnity from NVIDIA for downstream commercial deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model trained on internet-crawled data containing toxic language and societal biases; may amplify these in outputs. No adversarial robustness evaluation disclosed. NVIDIA provides a security vulnerability reporting channel (Intigriti VDP). Quantization (FP4) may reduce attack surface relative to full-precision, but no formal security audit described. Users must implement input validation, output filtering, and guardrails for sensitive applications. Supply-chain: quantization performed by NVIDIA; base model from DeepSeek (third-party trust required).
Alternatives to consider
DeepSeek-V4-Pro (full precision)
Original unquantized model; higher accuracy potential but requires 2–4× VRAM and more inference compute. Choose if memory/cost is secondary to peak performance.
Meta Llama 3.1 405B quantized
Alternative large-scale open model with established vLLM/llama.cpp support and broader community tooling. No MoE; different reasoning/agentic profile; consider if tool-use ecosystem maturity is prioritized.
Qwen2.5-Max or Qwen QwQ (quantized)
Chinese-origin models with similar scale and reasoning capabilities, potential better multilingual support. Quantization profiles and serving maturity vary; verify integration readiness for target inference stack.
Ship DeepSeek-V4-Pro-NVFP4 with senior software developers
DeepSeek-V4-Pro-NVFP4 enables enterprise-scale agentic reasoning and long-context processing on Blackwell. Get guidance on cluster sizing, guardrail implementation, and regulatory compliance from our AI specialists.
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DeepSeek-V4-Pro-NVFP4 FAQ
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DeepSeek-V4-Pro-NVFP4 enables enterprise-scale agentic reasoning and long-context processing on Blackwell. Get guidance on cluster sizing, guardrail implementation, and regulatory compliance from our AI specialists.