Implement · DeepSeek-R1-Distill
Can you run it?
Ownership levelSubstantialnone·limited·partial·substantial·fullAnalytical input C ยท 64.8/100
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 Ollama, llama.cpp, vLLM, or SGLang. The small sizes run on a single consumer GPU.
Pin the exact revision and verify checksums.
Hardware & VRAM requirements
Small dense models: 1.5B-7B on consumer GPUs or CPU; 14B/32B on a single data-centre GPU, or
high-end consumer hardware when quantized. 128K context (inherited from the Qwen2.5 bases).
The easiest DeepSeek reasoning weights to deploy.
Serving stacks
First-class support across vLLM, SGLang, llama.cpp, and Ollama - small enough
for edge / on-device.
Safe-deployment controls & Deployment Ceiling
Deployment Ceiling: T2 (conditional). These have no safety tuning of their own, 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 the
China-aligned filtering inherited from R1.
Available quantizations
The most extensively quantized DeepSeek artifacts - GGUF and lower-bit across all sizes.
Apache-2.0 permits redistribution of these derivatives; still, verify provenance per mirror.
Fine-tuning & adaptation
Apache-2.0 permits unrestricted fine-tuning and further distillation, and the small sizes
make it cheap. The distillation data and code are closed, so there is no from-scratch
reproduction of the distillation itself.
API / OpenAI-compatible integration
Serve behind an OpenAI-compatible endpoint (vLLM / SGLang / Ollama) and point your client at
it; handle the reasoning / think-tag output format at the client.
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?
StrongPerformance 3, operational 5 and safety 3: solid reasoning-for-size with the easiest serving in the family; all three at or above 3, operational at 5, so reliability is strong. The caveat is the absence of own safety tuning / a first-party guard model.
How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionThe easiest DeepSeek weights to run: small dense models with first-class serving (vLLM, SGLang, llama.cpp, Ollama), the most widely mirrored and quantized DeepSeek artifacts, deployable on consumer hardware.
Safety3/5whether misuse risks are evaluated and guardrails are providedDocumented reasoning behaviour meeting the score-3 anchor, but these are research distils with no safety tuning of their own, inheriting R1's 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?
StrongSelf-hosted, the Apache-2.0 weights run entirely on your own infrastructure - including on-device at the small sizes - with no telemetry and no claw-back, so your data stays yours.
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 these self-hosted Apache-2.0 R1-Distill (Qwen) models: a clean permissive licence makes use-and-modify strong, and self-hosting - including on-device at the small sizes - keeps your data yours, so data-control is strong too. Ownership is substantial, a step above the Llama-base distils. It stops short of full because the distillation data and code are closed (not reproducible) and the weights inherit R1's China-aligned filtering and carry no safety tuning of their own - deploy behind your own guardrails.
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-R1 README + the distil checkpoint cards, read: the R1-Distill-Qwen checkpoints are built on Qwen2.5 bases (1.5B/7B/14B/32B) and "are derived from ...
Model cardread2026-08-03
R1-Distill-Qwen model cards on the verified deepseek-ai HF org: dense 1.5B/7B/14B/32B models distilled from R1, 128K context (from the Qwen2.5 bases), safetensors; the most widely mirrored/quantized DeepSeek artifacts, serving on consumer hardware via Ollama, llama.cpp, vLLM, SGLang.
Third-party analysisunverified2026-08-03
On public leaderboards the R1-Distill-Qwen-14B/32B are notably strong reasoning models for their size; the 1.5B is modest.
Third-party analysisunverified2026-08-03
The R1-Distill checkpoints are research distils with no safety tuning of their own and inherit R1's China-aligned content filtering; no companion guard model ships.
Third-party analysisunverified2026-08-03
The R1-Distill-Qwen checkpoints are small (1.5B-32B), well below the systemic-risk threshold; their Apache-2.0 licence is FOSS, so the open-source exemption applies to the transparency duties, but DeepSeek publishes no copyright policy or training-content summary.