DeepSeek32B~20 GB VRAM at Q4_K_M

DeepSeek R1 Distill Qwen 32B — VRAM, speed & local setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated January 20, 2025

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.

Logic96

Creative80

Coding95

VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	13.5 GB	~57 tok/s	Fits
Q3_K_M 3.41 bpw	16.6 GB	~46 tok/s	Fits
Q4_K_M 4.83 bpw	22.3 GB	~34 tok/s	Tight
Q5_K_M 5.67 bpw	25.6 GB	~4 tok/s	Offload
Q6_K 6.56 bpw	29.2 GB	~4 tok/s	Offload
Q8_0 8.50 bpw	36.9 GB	~3 tok/s	Offload
F16 16.00 bpw	66.9 GB	—	Too big

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. DeepSeek R1 Distill Qwen 32B VRAM calculator →

Get DeepSeek R1 Distill Qwen 32B running

The cheapest catalogued GPU that runs DeepSeek R1 Distill Qwen 32B is the AMD Radeon RX 7900 XTX (24 GB).

Buy this hardware AMD Radeon RX 7900 XTX 24GB — 24 GB VRAM · 355 W board power Deploy in the cloud now RTX 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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AMD Radeon RX 7900 XTX 24GB

24 GB VRAM · 355 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

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 ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters: 32 Billion
Context window: 128,000
Architecture: Dense
Provider: DeepSeek
Licence: MIT
Practical deployment: Single consumer GPU
Specified at: Q4_K_M
System RAM: 32 GB
Record updated: 2025-01-20

LicenceMITCommercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forcodingmathcomplex 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.