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Implement · DeepSeek-V3

Can you run it?

Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C ยท 64.4/100

This page is a projection of the one entry record, the Reliability and Data control factors that Implement covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.

Which domain expands which factor
  • AssessUse & modify + Transparency
  • ImplementData control + Reliability
  • UseReliability
  • SupportTransparency

Install & run

Download the safetensors from the verified deepseek-ai organisation on Hugging Face and serve with vLLM, SGLang, llama.cpp, or Ollama. Pin the exact revision and verify checksums. Deploy the Chat variant for assistant use - the Base variant is an un-tuned completion model. Note the weight licence is the DeepSeek License Agreement, not MIT.

Hardware & VRAM requirements

A 671B-total / 37B-active MoE: even quantized the weights are large, so a full-fidelity deployment is a multi-GPU / multi-node exercise. The community quantization ecosystem (GGUF and lower-bit) makes reduced-precision serving tractable, at the usual quality trade-off. 128K context adds KV-cache pressure at long inputs.

Serving stacks

First-class support across vLLM, SGLang, llama.cpp, and Ollama.

Safe-deployment controls & Deployment Ceiling

Deployment Ceiling: T2 (conditional). First, honour the DeepSeek License use restrictions - they are a binding condition of the grant, not optional guidance. Then, as with the rest of the family: supply your own input/output guardrails and a guard model, add prompt-injection defences and treat retrieved/tool content as untrusted for agentic use, and account for China-aligned topic filtering. Never deploy the Base variant unwrapped.

Available quantizations

An extensive community quantization ecosystem circulates. Redistribution of these derivatives must carry the DeepSeek License field-of-use restrictions (not MIT terms) - verify provenance and prefer hosts you already trust.

Fine-tuning & adaptation

The licence permits fine-tuning and derivatives, but they inherit the field-of-use restrictions. The training data and code are closed, so there is no from-scratch reproduction - adaptation of the released weights is possible within the licence's limits.

API / OpenAI-compatible integration

Serve the Chat variant behind vLLM's or SGLang's OpenAI-compatible endpoint and point your existing client at it - a standard instruct interface.

How this scores

The ownership factors this domain covers, drawn from the one entry record.

3

ReliabilityIs it reliable and good enough for the job?

Strong

Performance 4, operational 5 and safety 3: a strong model with first-class serving; all three at or above 3 with two at or above 4, so reliability is strong. The caveat is the absence of a first-party guard model (and the un-tuned Base variant).

How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionFirst-class ecosystem support: broad serving across vLLM, SGLang, llama.cpp and Ollama, an extensive family of community quantizations, and wide third-party hosting.
Safety3/5whether misuse risks are evaluated and guardrails are providedThe Chat variant is safety-tuned with documented behaviour, meeting the score-3 anchor, but safety tuning is lighter than Western frontier labs, the model exhibits China-aligned topic censorship, and no first-party guard model ships - deployers must add their own guardrails.
4

Doesn't extract your dataDoes running it keep your knowledge and data yours?

Strong

Self-hosted, the weights run entirely on your own infrastructure with no telemetry and an irrevocable grant, so your data stays yours and access cannot be clawed back. (The hosted DeepSeek API is a separate matter with its own data terms.)

How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
What this means for adoptionYou partially own self-hosted original DeepSeek-V3: self-hosting keeps your data yours under an irrevocable grant (data-control strong, reliability strong), but the DeepSeek License Agreement imposes RAIL-style field-of-use restrictions that flow down to derivatives, holding use-and-modify to moderate - so ownership is partial, one step below the MIT V3 generations (deepseek-v3-mit), which reach substantial. For new work prefer the MIT generations unless you specifically need this checkpoint; where you do use it, honour the use restrictions, deploy behind your own guardrails, and treat EU high-stakes use as needing a self-assembled compliance package (with no open-source exemption on the licence axis).

Sources

The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.

Licenceread2026-08-03
DeepSeek-V3 LICENSE-CODE / LICENSE-MODEL, read: "This code repository is licensed under the MIT License.
Model cardread2026-08-03
DeepSeek-V3 model card on the verified deepseek-ai HF org: 671B total / 37B active MoE, 128K context, safetensors, Base + Chat variants; first-class serving (vLLM, SGLang, llama.cpp, Ollama) and an extensive community quant ecosystem.
Third-party analysisunverified2026-08-03
On independent public leaderboards the original DeepSeek-V3 was competitive among large open-weight models at release (coding, maths, analysis); not OneHill-reproduced this session.
Third-party analysisunverified2026-08-03
Independent analysis notes DeepSeek open-weight models apply China-aligned content filtering on politically sensitive topics, with lighter safety tuning than Western frontier labs and no companion guard model.
Third-party analysisunverified2026-08-03
The DeepSeek License Agreement imposes field-of-use restrictions (non-FOSS), and no public EU AI Act training-content summary, copyright policy, or GPAI documentation package is published for DeepSeek-V3; the training corpus is not released.