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Implement · DeepSeek-V3

Can you run it?

Ownership levelSubstantialnone·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 (V3-0324 or V3.1) and serve with vLLM, SGLang, llama.cpp, or Ollama. Pin the exact revision and verify checksums before loading. Confirm you are on a V3-0324-or-later checkpoint for the MIT grant.

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 extensive community quantization ecosystem (GGUF and lower-bit) makes reduced-precision serving tractable on smaller footprints, 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, with wide third-party hosting shortly after release.

Safe-deployment controls & Deployment Ceiling

Deployment Ceiling: T2 (conditional). No first-party guard model ships and safety tuning is lighter than at Western frontier labs, so supply your own input/output guardrails and a guard/classifier model, add prompt-injection defences and treat retrieved/tool content as untrusted for agentic use, and account for China-aligned topic filtering on sensitive prompts. With those controls in place it is deployable for general-purpose assistant use; without them it is not customer-safe.

Available quantizations

An extensive community quantization ecosystem (GGUF and lower-bit variants) circulates. MIT permits redistribution of these derivatives, but each is a separate artifact whose trust equals its uploader - verify provenance and prefer hosts you already trust.

Fine-tuning & adaptation

MIT permits unrestricted fine-tuning and distillation of the released weights. The training data and code are closed, so there is no from-scratch reproduction, but adaptation of the V3-0324 / V3.1 weights is unrestricted.

API / OpenAI-compatible integration

Serve behind vLLM's or SGLang's OpenAI-compatible endpoint and point your existing client at it. V3.1 exposes a hybrid reasoning mode - handle its output convention at the client, separating any reasoning segment from the final answer.

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 instruct 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.

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 shortly after release.
Safety3/5whether misuse risks are evaluated and guardrails are providedReleased as safety-tuned instruct variants 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 MIT weights run entirely on your own infrastructure with no telemetry and no licence claw-back, so your data stays yours. (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 substantially own the self-hosted MIT DeepSeek-V3 generations: MIT permits commercial use and modification, and self-hosting keeps your data yours, so use-and-modify and data-control are both strong - ownership is substantial. It stops short of full because the training corpus and code are closed (not reproducible), the weights carry China-aligned filtering you cannot inspect, and there is no first-party guard model. Note the original Dec-2024 V3 weights are the more restrictive deepseek-v3-original entry - confirm you are on a V3-0324-or-later MIT checkpoint. Deploy behind your own guardrails, and treat EU high-stakes use as needing a self-assembled compliance package.

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-V3-0324 model card / LICENSE, read: the V3-0324 (and later V3.1) weights are licensed under the MIT License - "This code repository and the model weights are licensed under the MIT License." This is a change from the original December-2024 DeepSeek-V3, whose weights use the custom "DeepSeek License Agreement, Version 1.0".
Model cardread2026-08-03
DeepSeek-V3-0324 model card on the verified deepseek-ai HF org: 671B total / 37B active MoE, 128K context, safetensors; V3.1 is a later hybrid-reasoning generation on the same org; first-class serving (vLLM, SGLang, llama.cpp, Ollama) and an extensive community quant ecosystem.
Third-party analysisunverified2026-08-03
On independent public leaderboards the MIT DeepSeek-V3 generations are competitive among large open-weight instruct models (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
No public EU AI Act training-content summary, copyright policy, or GPAI documentation package is published for DeepSeek-V3, and the training corpus is not released.