Open-Weight LLM · Private & Custom AI
DeepSeek-V4-Pro-NVFP4
A 1.6T-parameter MoE model quantized to 4-bit for private deployment—built for reasoning-heavy ops automation, agentic workflows, and custom AI on your own infrastructure.
DeepSeek-V4-Pro-NVFP4 is NVIDIA's quantized version of DeepSeek's flagship reasoning model, compressed to FP4 weights without significant accuracy loss. For ops teams, this means you can run a 1.6T-parameter reasoning engine on private Blackwell GPUs—ideal for automating complex decision-making, tool-use workflows, and long-context document processing without sending data to external APIs.
Model facts
Private deployment
Run DeepSeek-V4-Pro-NVFP4 in your own environment
Run it on-premise or in your VPC via vLLM or SGLang on NVIDIA Blackwell hardware (tested on B200). The model is quantized to NVFP4 (4-bit weights + 8-bit activations in linear layers), which trades inference cost for minimal accuracy drop. Self-hosting means all inputs, outputs, and internal state remain in your environment—no third-party logging or data transit.
Operational AI use cases
Agentic Customer Support Automation
Route inbound support tickets through the model's tool-use and policy-adherence capabilities (τ²-Bench Telecom evals 94.83% accuracy). The model can query internal systems, apply business rules, and draft or auto-resolve tickets without human handoff. Run 1M-token context to process full ticket histories + knowledge bases in a single call.
Long-Context Document & Knowledge Processing
Feed contracts, regulations, RFPs, or internal wikis (up to 1M tokens) into a single inference. Use reasoning modes (Think High for analysis, Think Max for deep reasoning) to extract compliance gaps, summarize key terms, or flag risks. Supports JSON output for downstream workflow integration.
Intelligent Workflow Orchestration & Code Generation
Automate ops workflows by reasoning over structured requests: parse ops tickets, decide escalation paths, generate SQL/API calls, validate logic. SciCode evals show 53.45% accuracy; instruction-following (IFBench 77.21%) ensures reliable control. Deploy as a private decision engine behind internal APIs.
Custom AI
As a base for custom AI
Excellent base for custom AI products: fine-tune on proprietary ops data (support patterns, domain language, internal logic) or use as-is for retrieval-augmented reasoning. The 49B activated parameters (sparse MoE) keep inference cost reasonable; the 1M token context and structured output support let you build domain-specific reasoning layers on top without external model calls.
In the operating system
Where it fits
Sits in the **agent & reasoning layer** of an AI OS—receives structured requests from workflow automation, applies reasoning over long contexts (policies, docs, logs), and returns actionable output (decisions, code, summaries) for downstream task execution. Can feed into knowledge retrieval layers and plug directly into ops task queues.
Data control & security
Self-hosting ensures all inference data—prompts, internal docs, customer details, reasoning traces—stays in your network. No data leaves your environment; no external API logging. This is an architectural choice, not a property of the model itself. You remain responsible for access controls, data retention policies, and output safety guardrails before integration.
Hardware footprint
**Estimate (unverified):** ~120–160 GB VRAM for single-GPU inference at FP4 (weights only); 180–240 GB with KV-cache for 1M-token context and batching. Tensor-parallel across 4–8 B200s typical for production. Consult NVIDIA sizing docs for your exact workload.
Integration
Deploy via vLLM (tensor-parallel across 4–8 Blackwell GPUs) or SGLang (auto-detects NVFP4 from hf_quant_config.json). Expose via OpenAI-compatible API or custom gRPC. Ingest structured JSON requests (with system/user/tool prompts); parse JSON or structured text responses. Requires Linux + NVIDIA CUDA + trust-remote-code flag. Integrate with ops platforms (ticketing, docs, CRM) via webhooks or scheduled batch inference.
When it's not the right fit
- —You need sub-millisecond latency: MoE routing + reasoning modes add latency; best for batch/async ops tasks.
- —Your workload is not reasoning-heavy: a smaller, specialized model (e.g., Llama 8B fine-tuned for your domain) will be cheaper and faster for rote classification or summarization.
- —You lack Blackwell GPU access: the model is optimized for Blackwell; older NVIDIA hardware will incur performance/cost penalties; running on CPU is not practical.
- —You need absolute accuracy guarantees: the model was trained on internet data with biases and toxic language; requires careful prompt design and output guardrails before customer-facing deployment.
Alternatives to consider
Llama 3.1 405B (Meta)
Larger dense model; better for long-context retrieval but higher VRAM; not optimized for quantization. Open license, but no built-in reasoning modes.
Qwen2.5 72B (Alibaba)
Smaller, faster, lower VRAM (~96GB FP8); strong for ops tasks, coding, instruction-following. Easier to self-host on smaller clusters; trade-off: less reasoning depth than V4-Pro.
Mixtral 8x22B (Mistral)
Also MoE; 141B params with 39B active; widely quantized. Lighter to deploy than V4-Pro; weaker on reasoning benchmarks but solid for ops automation on constrained hardware.
FAQ
Can I fine-tune this model on my company's internal data?
The MIT license permits it. However, fine-tuning a 1.6T MoE model is expensive. Start by in-context learning (system prompts + examples) on the base model, then evaluate if task-specific fine-tuning is needed. Check NVIDIA/DeepSeek docs for LoRA or other parameter-efficient methods compatible with NVFP4.
Is this commercially usable without restrictions?
Yes. The model card states 'ready for commercial/non-commercial use' and is licensed MIT (permissive OSI license). You can use it in production, in products, or for service delivery. Ensure you have proper data handling agreements and comply with your industry's regulations (e.g., HIPAA, PCI-DSS if handling sensitive data).
What's the difference between running this privately vs. using an API?
Private: all data stays in your environment; lower per-call cost at scale; full control over inference settings, batching, and uptime; no cold starts; requires GPU infrastructure upfront. API: pay per token, no infra management, but data transits to provider, subject to their retention policies, and you lose inference tracing. For ops automation, private typically wins on cost and data control.
How do I decide between reasoning modes (Non-think, Think High, Think Max)?
Non-think: fast, low latency; use for high-volume ops like ticket classification. Think High: moderate reasoning for policy checks or multi-step logic. Think Max: deep reasoning for complex analysis; slowest. Start with Non-think; upgrade latency budget if accuracy suffers.
Build Your Private AI Operating System
DeepSeek-V4-Pro-NVFP4 is a foundation model—not a complete AI OS. At LLM.co, we help middle-market companies architect and deploy custom reasoning layers, ops workflows, and data-controlled AI infrastructure. Let's design your private AI stack. Schedule a brief call with an ops AI specialist.