Reasoning

Check exact model cardOpen weights where releasedUpdated August 2026

DeepSeek R1 Distill Qwen 32B

DeepSeek R1 Distill Qwen 32B is a distilled DeepSeek R1 variant for reasoning evaluation with more accessible deployment options than the full model.

DeepSeek · DeepSeek

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesOpenSourcesAI checker catalog + Hugging Face model card (deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Reasoning experiments, local-friendly evaluation, and distilled model comparisons.

Who should use it

  • Reasoning experiments, local-friendly evaluation, and distilled model comparisons.
  • Builders who want local or self-hosted testing options.

Common workflows

  • Distilled reasoning and local evaluation
  • reasoning workflows
  • distilled workflows
  • local workflows
  • open weights workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for DeepSeek workflows.
  • Reasoning experiments, local-friendly evaluation, and distilled model comparisons.
  • Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: 32,768 tokens.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Will DeepSeek-R1 Distill Qwen 32B run on your machine?

DeepSeek-R1 Distill Qwen 32B is 32B parameters and needs 21.5 GB of VRAM at Q4_K_M — 20 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M20 GB21.5 GBgood
Q8_034 GB35.5 GBhigh
FP1664 GB65.5 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBToo large
RTX 4060 Laptop8 GB16 GBToo large
RTX 3060 (12GB)12 GB32 GBCPU offload
RTX 4060 Ti (16GB)16 GB32 GBCPU offload
RTX 309024 GB64 GBComfortable
Apple Silicon (Unified Memory) 36 GB27 GB of 36 GB36 GBComfortable
RTX 509032 GB64 GBComfortable

Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.

Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.

Check DeepSeek-R1 Distill Qwen 32B against your exact hardwareHow these verdicts are calculated

VRAM fit by quantization level

Enter your GPU VRAM below to see which quantization of DeepSeek R1 Distill Qwen 32B fits and get the Ollama run command.

Sources to verify

OpenSourcesAI checker catalog + Hugging Face model card (deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

DeepSeek family hubModel selection guideFrontier vs open-weight contextLocal coding assistant stackAI tools directory
Hardware~20 GB at Q4_K_M (32B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext32,768 tokensLast updated2026
OpenSourcesAI checker catalog + Hugging Face model card (deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What can 12GB VRAM run? →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

DeepSeek family hub →DeepSeek R1 Distill Qwen 7B →DeepSeek V3 →DeepSeek-V4-Pro →DeepSeek-R1 Distill 14B →DeepSeek-R1 8B (0528 Qwen3) →DeepSeek Coder 33B →

Guides, stacks, and comparisons

Model selection guide →Frontier vs open-weight context →Local coding assistant stack →All model comparisons →

Related models

DeepSeek R1 Distill Qwen 7BReasoning experiments, local-friendly evaluation, and distilled model comparisons.DeepSeek V3General DeepSeek-family comparisons and assistant workflow evaluation.DeepSeek-V4-ProTeams comparing frontier-style open-weight reasoning and coding models against hosted closed models.DeepSeek-R1 Distill 14B14B open-weight model in the DeepSeek family.DeepSeek-R1 8B (0528 Qwen3)8B open-weight model in the DeepSeek family.DeepSeek Coder 33BCoding-model comparisons and legacy baseline testing.

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