DeepSeek-V3.1
DeepSeek-V3.1 is a 671B-parameter hybrid LLM supporting both standard and 'thinking' modes, enabling reasoning-intensive tasks without requiring separate model checkpoints. MIT-licensed and ungated, it excels at code generation, mathematics, and tool-use workflows. The model uses FP8 quantization compatibility and is production-ready via HuggingFace.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | deepseek-ai |
| Parameters | 684.5B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 279.2k |
| Likes | 825 |
| Last updated | 2025-09-05 |
| Source | deepseek-ai/DeepSeek-V3.1 |
What DeepSeek-V3.1 is
DeepSeek-V3.1 is a 671B-parameter mixture-of-experts model with 37B active parameters per token. Context window: 128K. Built from extended V3.1-Base via two-phase long-context training (32K phase: 630B tokens; 128K phase: 209B tokens). Supports hybrid inference: non-thinking mode (standard chat templates) and thinking mode (chain-of-thought reasoning similar to R1). Trained with UE8M0 FP8 quantization for weight and activation compatibility. Tool-calling and search-agent support integrated via specialized chat templates.
Run DeepSeek-V3.1 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3.1")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
ESTIMATE: For full precision (BF16): ~1.3 TB VRAM for single-GPU inference (not practical). For FP8 quantization: ~670–750 GB VRAM (e.g., 8× A100 80GB or 4× H100 80GB with sharding). Batch inference: 1–2 TB VRAM for competitive throughput. Requires NVLink or high-bandwidth interconnect. Local CPU inference not recommended.
Fine-tuning details not stated in model card. LoRA/QLoRA feasibility unknown; model size suggests parameter-efficient tuning (LoRA rank ~128–256) may be necessary to fit in available VRAM. Recommend reviewing DeepSeek's official guidance or community forks for proven fine-tuning recipes. Multi-turn chat template customization is supported via jinja2 chat_template configuration.
When to avoid it — and what to weigh
- Real-time or latency-critical systems — 671B parameters require significant VRAM and compute. Thinking mode adds inference latency compared to smaller models. Not suitable for sub-100ms response requirements.
- Constrained edge or embedded deployment — Model size and quantization requirements demand enterprise-grade GPUs (A100 80GB or similar). Consumer hardware or mobile inference not feasible without extreme quantization (which is untested here).
- Projects requiring guaranteed inference speed or predictable latency SLAs — Thinking mode is adaptive: cost-per-token varies based on problem complexity. Standard SLA commitments (e.g., 'all requests <500ms') cannot be made reliably.
- Use cases requiring proprietary model modifications or weight redistribution — MIT license permits modification and redistribution, but vendor lock-in on infrastructure (HuggingFace, proprietary serving layers) may complicate portability.
License & commercial use
MIT license. Permissive open-source; allows commercial use, modification, and redistribution with attribution. No copyleft obligations. Model weights and code fully open.
MIT is a permissive OSI-approved license. Commercial use, including deployment in for-profit services, is explicitly permitted. No royalties, license agreements, or usage restrictions apply. Attribute DeepSeek-AI as required by MIT. No gating or commercial terms to negotiate.
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 |
No explicit security audit, red-teaming, or safety benchmarks stated in card. Thinking mode may increase risk of prompt injection or jailbreak via chain-of-thought exposure. Tool-calling and agent modes require careful prompt engineering to prevent code injection or unintended API calls. Deployment should include input/output filtering, rate limiting, and audit logging. FP8 quantization may affect model behavior vs. full precision—validate on sensitive tasks before production. No information on data provenance or model contamination checks.
Alternatives to consider
Meta Llama 3.1 (405B)
Open-source, similar scale, strong code/math benchmarks. Llama 3.1 license (custom, non-commercial for model weights) requires review for production use. No thinking mode; simpler inference.
Qwen 2.5 (72B–QwQ-32B reasoning variant)
Smaller, MIT-licensed alternative with reasoning capability. Fits on single A100/H100. Trade-off: lower absolute performance on complex tasks, but faster inference and lower ops cost.
Claude 3.5 Sonnet (via API)
Closed-source, best-in-class reasoning and code. No self-hosting; vendor lock-in. Suitable if inference cost and latency SLAs can tolerate API dependency and per-token pricing.
Ship DeepSeek-V3.1 with senior software developers
DeepSeek-V3.1 is free, open-source, and commercially usable under MIT. Start with a test deployment on your infrastructure or contact us to evaluate fit for your use case.
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DeepSeek-V3.1 FAQ
Can I use DeepSeek-V3.1 in a commercial product?
What is the minimum hardware to run this model?
How does thinking mode affect inference cost and latency?
Is fine-tuning supported?
Work with a software development agency
Need help beyond evaluating DeepSeek-V3.1? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to deploy a reasoning-capable LLM?
DeepSeek-V3.1 is free, open-source, and commercially usable under MIT. Start with a test deployment on your infrastructure or contact us to evaluate fit for your use case.