DeepSeek R1 Distill Qwen 32B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated January 20, 2025

Model library → DeepSeek R1 → DeepSeek R1 Distill Qwen 32B

The sweet spot for high-performance reasoning. Beats many 70B models in math and code.

DeepSeek R1 Distill Qwen 32B needs about 20 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters32 Billion
Context window128,000
ArchitectureDense
ProviderDeepSeek
LicenceMIT
Specified atQ4_K_M
System RAM32 GB
Record updated2025-01-20

Licence

MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K10.5 GB13.5 GB~57 tok/s (est.)Fits comfortably
Q3_K_M13.6 GB16.6 GB~46 tok/s (est.)Fits comfortably
Q4_K_M19.3 GB22.3 GB~34 tok/s (est.)Tight fit
Q5_K_M22.7 GB25.6 GB~4 tok/s (est.)Offloads to system RAM (slow)
Q6_K26.2 GB29.2 GB~4 tok/s (est.)Offloads to system RAM (slow)
Q8_034.0 GB36.9 GB~3 tok/s (est.)Offloads to system RAM (slow)
F1664.0 GB66.9 GB—Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the DeepSeek R1 Distill Qwen 32B VRAM calculator.

Buy This HardwareAMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs DeepSeek R1 Distill Qwen 32B is the AMD Radeon RX 7900 XT (20 GB).

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AMD Radeon RX 7900 XT 20GB
20 GB VRAM · 315 W board power
2026 prices are volatile — check the current listing.
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How to Run DeepSeek R1 Distill Qwen 32B

Install Ollama, then run:

ollama run deepseek-r1:32b

Weights on Hugging Face: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B.

Best for: coding, math, complex reasoning.

Can I Run DeepSeek R1 Distill Qwen 32B on My GPU?

Other DeepSeek R1 Sizes

DeepSeek R1 Distill Qwen 32B — Frequently Asked Questions

How much VRAM does DeepSeek R1 Distill Qwen 32B need?
About 20 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does DeepSeek R1 Distill Qwen 32B run on an RTX 4090 (24 GB)?
Yes. DeepSeek R1 Distill Qwen 32B needs about 20 GB at Q4_K_M, inside a 24 GB card, at an estimated 34 tokens/sec.
How do I run DeepSeek R1 Distill Qwen 32B locally?
Install Ollama and run `ollama run deepseek-r1:32b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does DeepSeek R1 come in?
DeepSeek R1 Distill Llama 8B (6 GB), DeepSeek R1 Distill Qwen 32B (20 GB), DeepSeek R1 Distill Qwen 14B (9 GB), DeepSeek R1 (671B) (406 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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