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text model · DeepSeek-R1-Distill · Windows

Can I run DeepSeek-R1-Distill-Qwen 32B on Nvidia GeForce RTX 4090 (24GB)?

Compatibility verdict VRAM threshold engine
Yes, but tight GPU accelerated ~33 tok/s est.

Yes. DeepSeek-R1-Distill-Qwen 32B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~22.1 GB of ~23 GB usable).

Needs ~22.1 GB Device usable ~23 GB

Fits at Q4_K_M (~22.1 GB of ~23 GB usable) but with little headroom. Close other apps; a smaller context frees a few hundred MB.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4090 (24GB) leaves ~0.9 GB of headroom.

Q4_K_M needed
~22.1 GB
Usable on device
~23 GB
Device memory
24 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~23 GB usable
Q2_K
~15.6 GB
Q3_K_M
~17.8 GB
Q4_K_M
~22.1 GB
Q5_K_M
~25 GB
Q6_K
~28.4 GB
Q8_0
~37 GB
FP16
~66.2 GB
The line marks Nvidia GeForce RTX 4090 (24GB)'s ~23 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~450 W
Electricity / 1M tokens
~$0.57

At ~$0.15/kWh and the estimated ~33 tok/s, a million generated tokens costs about $0.57 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run deepseek-r1:32b
llama.cpp
$ llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
LM Studio
$ lms get bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF
Model DeepSeek-R1-Distill
Parameters
32B
Q4_K_M size
19.85 GB
Q8_0 size
34.82 GB
Context
128k
Ollama tag
deepseek-r1:32b
Full DeepSeek-R1-Distill-Qwen 32B requirements →
Device Windows
Memory
24 GB vram
Usable for weights
~23 GB
Power draw
~450 W
Best runtime
vLLM (Linux) / Ollama (CUDA)
Best models for Nvidia GeForce RTX 4090 (24GB) →

You could also run

Run DeepSeek-R1-Distill-Qwen 32B on other hardware

FAQ

Can Nvidia GeForce RTX 4090 (24GB) run DeepSeek-R1-Distill-Qwen 32B?

Yes. DeepSeek-R1-Distill-Qwen 32B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~22.1 GB of ~23 GB usable).

How much memory does DeepSeek-R1-Distill-Qwen 32B need?

It is a tight fit on Nvidia GeForce RTX 4090 (24GB). At Q4_K_M the weights are ~19.85 GB; with KV cache and runtime overhead, budget ~22.1 GB at a 4k context.

What is the best tool to run DeepSeek-R1-Distill-Qwen 32B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.