Open-Weight LLM · Private & Custom AI
DeepSeek-R1-0528-NVFP4-v2
FP4-quantized 393B reasoning model for private deployment—cut VRAM needs ~40% vs FP8, maintain high reasoning accuracy, run on your own GPUs.
DeepSeek-R1-0528-NVFP4-v2 is NVIDIA's FP4-quantized version of DeepSeek's flagship reasoning model, optimized for TensorRT-LLM inference. A 393B parameter transformer excelling at math, code, and multi-step reasoning, it trades ~1% accuracy for 1.6x smaller memory footprint and faster inference. For ops teams building private AI systems, this is the reasoning backbone—when you need multi-hop logic and can't send queries to external APIs.
Model facts
Private deployment
Run DeepSeek-R1-0528-NVFP4-v2 in your own environment
Deploy on your own NVIDIA GPU cluster (B200 tested; Blackwell architecture required). Requires TensorRT-LLM (built from source), 8×B200 minimum for full parallelism (estimates ~130–160 GB VRAM in FP4 across cluster). MIT license + gated=false means no approval delays. Trade-off: locked to NVIDIA hardware and TensorRT stack; portability is limited. Architecture advantage: all reasoning, embeddings, and outputs stay in your data center—no external API calls, no data escrow.
Operational AI use cases
Multi-Step Financial & Compliance Analysis
Route complex audit queries, regulatory interpretation, and contract clause analysis to R1 running privately. Model excels at reasoning tasks (MATH-500: 98, AIME: 88). Finance & Legal teams avoid sending sensitive documents to third-party APIs; reasoning stays on-prem. Calibrated on news/summaries; can be fine-tuned on internal frameworks.
Internal Knowledge & Process Automation Agent
Build an agentic backbone for ops teams: ticket triage, runbook recommendation, incident root-cause analysis. R1's reasoning strength handles multi-step troubleshooting (LiveCodeBench 77, code reasoning). Run end-to-end in your environment; no data leaves the perimeter. Fine-tune on internal docs/SOPs for domain specificity.
R&D & Technical Documentation Generation
Use for internal technical writing, design review feedback, and architecture decision capture. GPQA Diamond (80) and SCICODE (44) show coding/scientific reasoning. Keeps IP-sensitive research on private infrastructure. No rate limits, no usage tracking—run inference as often as needed for internal iteration.
Custom AI
As a base for custom AI
Strong base for fine-tuning custom reasoning products. Quantization preserves enough fidelity (FP4 vs FP8 <1% gap on benchmarks) that domain-specific tuning (LoRA, full fine-tune on your proprietary data) will pay off. MIT license allows product wrapping. Start from this checkpoint, add your ops workflows/domain data, deploy as a private SaaS or internal tool. The quantization also means you can afford to run it on fewer GPUs than full-precision DeepSeek-R1, lowering TCO for continuous fine-tuning cycles.
In the operating system
Where it fits
Core reasoning layer in a private AI operating system. Sits above knowledge retrieval (RAG) and below workflow orchestration. In an LLM.co-style stack: feeds agents (decision logic), powers workflow state-machine steps (complex rule evaluation), enriches knowledge (generates summaries, extracts insights from long docs). FP4 quantization means you can run it on the same cluster as lighter models for inference load-balancing—trade accuracy for speed on simple tasks, route hard reasoning to R1.
Data control & security
Self-hosting is an architecture choice: all inference, prompts, and generated text stay on your infrastructure—no transmission to external LLM providers, no logging by NVIDIA, no third-party data processing agreements needed. This is compliance-relevant for finance, healthcare, legal, and R&D. Quantization is applied by NVIDIA (transparent, open-source calibration on CNN-DailyMail), not a security obfuscation. You must secure your GPU cluster, manage access controls, and audit model outputs yourself—quantization doesn't 'encrypt' data or provide built-in compliance.
Hardware footprint
**Estimate (FP4 weights + activations):** ~130–160 GB total across 8×B200 with tensor parallelism. Per-GPU: ~16–20 GB during inference (depends on batch size, context length). Compared to FP8 (~210–240 GB), FP4 saves ~40% VRAM. Requires Blackwell GPU (B100/B200); no support for A100, H100, or older. Context length: unknown—check DeepSeek-R1-0528 base model card; likely 4K–64K range.
Integration
TensorRT-LLM is the only supported inference engine (no vLLM, no ollama, no standard HF transformers.generate at scale). Requires: (1) NVIDIA CUDA 12.x + TensorRT libraries, (2) custom model compilation step before first inference, (3) tensor-parallel setup (8 GPUs recommended for 393B). API: use TensorRT-LLM's Python LLM class or gRPC server. Input: plain text (no system prompt per DeepSeek recommendations). Output: generated text string. For internal tooling: wrap in FastAPI, connect to ticketing/docs platforms via webhooks. Prompt engineering: temperature 0.5–0.7 (0.6 suggested), avoid system prompts, use step-by-step directives for math/code.
When it's not the right fit
- —You need strict inference latency SLAs <100ms per token—393B reasoning model will be slow on real hardware (estimate 10–50 ms/token depending on cluster tuning).
- —Your infrastructure is non-NVIDIA (AMD, Intel, TPU)—TensorRT-LLM and Blackwell requirement locks you in.
- —You need long-context retrieval (RAG) as primary use—check base model context length first; if <32K, supplement with dedicated embedding/retrieval layer.
- —You cannot afford 8×B200 upfront (~$1M+ capex)—this is enterprise-tier reasoning. Smaller quantized models or API-first alternatives may be cheaper per operation.
Alternatives to consider
Meta Llama 3.3 70B (GGUF/vLLM)
Lighter reasoning (70B vs 393B), broader runtime support (CPU, any GPU via vLLM), commercial-friendly, but weaker on MATH/AIME. Better for general ops tasks, worse for deep reasoning.
Mixtral 8x22B MoE
Sparse 141B effective params, lower VRAM than dense 393B, strong reasoning, multi-language. Inference library support broader than TensorRT-only. Trade: slightly lower math/code benchmarks than R1.
Qwen2.5 32B (quantized)
Compact, excellent engineering efficiency, supports vLLM/ollama natively, strong coding. No reasoning specialization; better for workflow automation, worse for mathematical reasoning.
FAQ
Can I run this model on my existing GPU infrastructure (A100, H100, etc.)?
No. TensorRT-LLM + FP4 quantization are Blackwell-only (B100/B200). You'd need to either migrate to NVIDIA's latest hardware or use an unquantized FP8 checkpoint with different inference libraries—but that doubles VRAM. If you're locked into older hardware, consider Mixtral 8x22B or smaller quantized Llama/Qwen models.
Is this MIT license—can I use it commercially without restrictions?
Yes. MIT is permissive: commercial use, modification, and private deployment are allowed. No attribution or license file inclusion required in runtime (though good practice to document). The model card states 'ready for commercial/non-commercial use.' No gating or approval process. You own the deployment; responsible for model outputs, biases, and compliance with your industry regulations.
What's the difference between FP4 v2 and the original FP4 version?
v2 additionally quantizes the 'wo' (output projection) modules in attention layers, reducing size/VRAM further. Both use FP4 data type. Benchmarks are nearly identical (see table: MMLU-Pro 84 vs 85, MATH-500 98 both). Choose v2 for slightly smaller footprint; either will work for most ops tasks. Full base model is FP32—these quantized versions are tuned for inference only, not training.
How do I fine-tune this on proprietary ops data?
Quantized FP4 checkpoints are inference-only. To fine-tune, start from the unquantized DeepSeek-R1-0528 base model (deepseek-ai/DeepSeek-R1-0528 on HF). Use standard PyTorch/HF transformers tooling + LoRA (PEFT library) or full fine-tune on your data. Post-training, requantize to FP4 via TensorRT Model Optimizer (nvidia-modelopt v0.33.0) for deployment. This pipeline ensures you get domain-specific reasoning without retraining from scratch.
Run Enterprise Reasoning On Your Own Servers
DeepSeek-R1-0528-NVFP4-v2 is your reasoning backbone. LLM.co helps you deploy it privately, connect it to your ops workflows, and fine-tune it on internal data—no API calls, no vendor lock-in. Let's build your custom AI system.