Implement · DeepSeek-R1-Distill
Can you run it?
Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C ยท 61.6/100
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 8B runs on a single consumer GPU. Pin the
exact revision and verify checksums. Note the weight licence is the Llama Community Licence.
Hardware & VRAM requirements
8B on a single consumer GPU; 70B on a single data-centre GPU, or high-end consumer hardware
when quantized. 128K context. The Llama base gives the broadest hardware and toolchain support
of any distil base.
Serving stacks
First-class support across vLLM, SGLang, llama.cpp, and Ollama - the Llama
base has the widest toolchain coverage in the ecosystem.
Safe-deployment controls & Deployment Ceiling
Deployment Ceiling: T2 (conditional). First, honour the Llama Community Licence - the
Acceptable Use Policy, the 700M-MAU clause, and the "Built with Llama" naming/attribution are
binding conditions. Then: supply your own input/output guardrails and a guard model (these
have no safety tuning of their own), 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
Extensively quantized (GGUF and lower-bit) via the Llama ecosystem. Redistribution of these
derivatives must carry the Llama licence and AUP - verify provenance per mirror.
Fine-tuning & adaptation
Fine-tuning and derivatives are permitted but inherit the Llama restrictions (AUP,
700M-MAU, and "Llama" naming/attribution). The distillation data and code are closed, so there
is no from-scratch reproduction of the distillation.
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 broadest serving support of the distils; 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 productionEasy to run: small dense models on a Llama base, which carries the broadest toolchain and hardware support of any distil base - first-class serving (vLLM, SGLang, llama.cpp, Ollama) and extensive quantization 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 weights run entirely on your own infrastructure - including on-device at 8B - with no telemetry and no claw-back, so your data stays yours. The licence restrictions constrain use, not data residency.
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 these self-hosted Llama-base R1-Distill models: self-hosting keeps your data yours (data-control strong, reliability strong), but the Llama Community Licence's acceptable-use policy, 700M-MAU clause, and naming/attribution duties hold use-and-modify to moderate - so ownership is partial, one step below the Apache-2.0 Qwen distils (deepseek-r1-distill-qwen), which reach substantial. For friction-free ownership prefer the Qwen distils unless you specifically need the Llama toolchain; where you use these, honour the Llama terms, deploy behind your own guardrails, and note there is no EU 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-R1 README + the distil checkpoint cards, read: the R1-Distill-Llama checkpoints are built on Meta Llama bases (Llama-8B on Llama 3.1, Llama-70B on Llama 3.3) and carry the Llama Community Licence - non-OSI, with an Acceptable Use Policy, the "700 million monthly active users" clause (a separate Meta licence required above it), and "Built with Llama" naming/attribution requirements.
Model cardread2026-08-03
R1-Distill-Llama model cards on the verified deepseek-ai HF org: dense 8B/70B models distilled from R1, 128K context, safetensors; widely mirrored/quantized through the Llama ecosystem, serving on consumer hardware via Ollama, llama.cpp, vLLM, SGLang.
Third-party analysisunverified2026-08-03
On public leaderboards the R1-Distill-Llama-70B is a notably strong reasoning model for its size; the 8B 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-Llama checkpoints are small (8B-70B), below the systemic-risk threshold; the Llama Community Licence is not FOSS, so no open-source exemption applies on the licence axis, and DeepSeek publishes no copyright policy or training-content summary.