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DeepSeek R1 Distill Qwen 14B

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

A larger distilled reasoning model for local systems with more memory headroom and a need for deeper bounded analysis.

Open pinned publisher source ↗Calculate local fit
Direct answer

Where DeepSeek R1 Distill Qwen 14B fits.

A larger distilled reasoning model for machines with more memory headroom.

The pinned DeepSeek repository identifies an MIT-licensed 14B conversational text-generation distillation.

Identity before opinion

The facts this release can support.

Exact model ID
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Release identity
R1 Distill / 1df8507178af
Parameters
14.77B 14,77,00,33,664 recorded
Documented context
1,31,072 tokens Publisher maximum, not a local-fit promise
Input → output
text → text
Weights access
Publicly discoverable files
Listed licence
MIT Read the complete publisher terms
API status
Not reviewed No API identity, price or data policy asserted
Use-case fit

Consider it when…

  • Compare reasoning quality against the smaller 7B distillation
  • Evaluate complex analysis with verifiable answers
  • Prototype private local reasoning where hardware permits
Limits and cautions

Check carefully when…

  • The larger working set can make interactive use slow on constrained machines
  • Long reasoning is not a substitute for factual verification
  • Maviter has not reproduced publisher benchmark claims
Transparent local planning

What 4-bit weights may ask of the machine.

These are Maviter calculations at an 8,192-token context—not publisher hardware requirements or proof that a runtime supports this release.

01 / Weight floor6.88 GiB

Parameters × 4 bits, converted to GiB.

02 / Runtime + KV3+ GiB

Runtime reserve and formula-backed KV cache.

03 / Planning floor10 GiB

Calculated working set rounded upward.

04 / Comfortable band16 GiB

A planning band with headroom—not a guarantee.

Before adoption

A three-check evaluation is more useful than a leaderboard.

Run the exact publisher release, on the intended runtime, with inputs from the work it must actually perform.

  1. 01Measure time-to-first-token and complete response time
  2. 02Use answer-level tests rather than judging reasoning style
  3. 03Compare the result with the 7B model on the same cases
Evidence ledger

Publisher sources, not copied binaries.

Pinned repository treedeepseek-ai/DeepSeek-R1-Distill-Qwen-14B1df8507178afcc1bef68cd8c393f61a886323761Open source ↗Pinned model cardCapabilities, licence and usage notesREADME.md at the recorded revisionRead card ↗

The evidence ledger pins an exact publisher revision and monitors it for drift. A source check does not independently reproduce benchmarks or certify production suitability.

Continue comparing
DeepSeek / 7.62BDeepSeek R1 Distill Qwen 7B

A seven-billion-parameter distilled reasoning model for local experiments where visible problem solving matters more than compact responses.

Open evidence brief →Qwen / 9.65BQwen3.5 9B

A nine-billion-parameter Qwen vision-language model for visual understanding, coding and agent-oriented local evaluation.

Open evidence brief →Mistral AI / 24.01BMistral Small 3.2 24B

A 24B multilingual instruction model with vision and function-calling examples for capable self-hosted systems.

Open evidence brief →
Common questions

Know the boundary before downloading.

Can DeepSeek R1 Distill Qwen 14B run locally?

The publisher provides discoverable weights, but runtime support and performance are machine-specific. Use Maviter's fit calculator, then run one controlled test.

Is this an API model?

This record documents downloadable weights. It does not assert a provider-hosted API ID, current price, rate limit or data policy.

Does Maviter host or redistribute the files?

No. The pinned links return to the publisher. Maviter does not mirror binaries or endorse unaudited third-party quantisations.