DeepSeek-R1-0528-NVFP4-v2
This is NVIDIA's FP4-quantized version of DeepSeek R1-0528, a 393B-parameter transformer LLM optimized for inference efficiency. The model compresses weights and activations to 4-bit precision using TensorRT Model Optimizer, reducing memory by ~1.6x compared to FP8. It is licensed under MIT and stated as ready for commercial and non-commercial use. Deployment requires TensorRT-LLM on NVIDIA hardware (tested on B200 Blackwell GPUs). The base model exhibits known limitations around toxic language and societal biases inherited from training data.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 393.6B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 847.4k |
| Likes | 23 |
| Last updated | 2025-09-02 |
| Source | nvidia/DeepSeek-R1-0528-NVFP4-v2 |
What DeepSeek-R1-0528-NVFP4-v2 is
DeepSeek-R1-0528-NVFP4-v2 is a post-training quantized variant of the open-source DeepSeek R1 base model. Quantization applies FP4 to weights and activations in linear operators within transformer blocks; other components remain unquantized. Calibrated on CNN/DailyMail dataset. Inference engine is TensorRT-LLM. Reference testing on B200 hardware. Evaluation benchmarks show <2% accuracy drop vs. FP8 baseline across math, reasoning, and coding tasks. Model card specifies temperature 0.5–0.7, no system prompts, and step-by-step reasoning for math.
Run DeepSeek-R1-0528-NVFP4-v2 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/DeepSeek-R1-0528-NVFP4-v2")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
Tested and optimized for NVIDIA Blackwell (B200). Card example uses 8x B200. Estimated VRAM per GPU: ~48–80 GB for model weights + activations + KV cache (estimate based on 393B params at FP4 ~50GB base + overhead; verify with TensorRT-LLM build). Linux OS required. TensorRT-LLM must be built from source with latest main branch per card instructions.
Not addressed in model card. Base model is a pre-trained, quantized checkpoint optimized for inference via TensorRT-LLM. Fine-tuning or continued training after FP4 quantization is not documented. Recommend: (1) assess whether fine-tuning the FP8 base model is feasible, then requantize, or (2) contact NVIDIA/DeepSeek for guidance on LoRA/QLoRA applicability to quantized checkpoints.
When to avoid it — and what to weigh
- Multi-GPU distribution unavailable — Model card example requires 8x B200 GPUs with tensor parallelism. Deployment without this hardware stack or TensorRT-LLM is unsupported and untested.
- Tolerance for bias and toxicity is low — Base model trained on internet data containing toxic language and societal biases. Card explicitly warns model may amplify biases and return toxic responses, especially with toxic prompts.
- System prompts or roleplay-centric workflows — DeepSeek recommends avoiding system prompts; all instructions must be in user prompt. Architectures relying on system-prompt conditioning may underperform.
- Non-NVIDIA infrastructure or non-Linux environments — Card specifies Linux as preferred OS and Blackwell/TensorRT-LLM as the supported runtime. Inference on other hardware (CPU, AMD GPUs, other inference engines) is unknown/untested.
License & commercial use
MIT license (OSI-compliant, permissive). Permits commercial and non-commercial use, modification, and distribution with appropriate attribution. No copyleft or commercial restrictions.
Model card states: 'This model is ready for commercial/non-commercial use.' MIT license is permissive and widely accepted for commercial deployments. However, card notes base model is third-party (DeepSeek-developed) and recommends developers work with internal teams to ensure compliance with industry and use-case requirements. No indemnification, warranty, or commercial SLA provided by NVIDIA. Verify with legal team before production 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 |
Card does not detail security posture of the quantization process, serialization format (safetensors), or supply-chain provenance. Safetensors format is generally safer than pickle. NVIDIA lists a security reporting channel. Base model trained on internet data; no data sanitization described. Recommend: (1) audit quantization code in TensorRT Model Optimizer, (2) validate safetensors files from official source, (3) run inference in isolated environment to contain potential toxic/biased outputs, (4) implement guardrails for production use.
Alternatives to consider
NVIDIA DeepSeek-R1-0528-FP8
Same base model, higher precision (FP8 vs. FP4), higher memory footprint (~2x vs. FP4) but potential accuracy gain on specialized tasks; consider if VRAM and cost allow.
Meta Llama 3.1 405B (or lower-param variants)
Open-source, MIT-licensed, widely supported inference stacks (vLLM, TGI, ollama). No quantization required at smaller scales; trade-off is different capability profile and smaller community adoption for reasoning tasks vs. DeepSeek R1.
OpenAI GPT-4 or similar closed-source APIs
If on-premises/self-hosted deployment is not mandatory, API-based closed models eliminate infrastructure overhead, offer commercial SLAs, and avoid bias/toxicity risks via fine-tuning; trade-off is vendor lock-in and per-token cost.
Ship DeepSeek-R1-0528-NVFP4-v2 with senior software developers
DeepSeek-R1-0528-NVFP4-v2 is production-ready for Blackwell-based infrastructure. Start with a proof-of-concept on TensorRT-LLM, benchmark against your workloads, and work with your legal/security teams to address bias/toxicity guardrails before production rollout.
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DeepSeek-R1-0528-NVFP4-v2 FAQ
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Ready to Deploy a Quantized LLM at Scale?
DeepSeek-R1-0528-NVFP4-v2 is production-ready for Blackwell-based infrastructure. Start with a proof-of-concept on TensorRT-LLM, benchmark against your workloads, and work with your legal/security teams to address bias/toxicity guardrails before production rollout.