DeepSeek R1 Distill Qwen 32B on NVIDIA GeForce RTX 4070 SUPER

Can the NVIDIA GeForce RTX 4070 SUPER run DeepSeek R1 Distill Qwen 32B?

54 pts

Barely: most of the model spills into system RAM

Heavy offload. At Q6_K it needs about 30.2 GB of 12.0 GB usable, with an estimated speed of 2.4 tok/s.

Best quant: Q6_K
Generation speed: 2.4 tok/s
Prompt processing: 409 tok/s
First token (1K prompt): 2.5 s
Largest safe context: —

Download DeepSeek R1 Distill Qwen 32B

Recommended for the NVIDIA GeForce RTX 4070 SUPER: Q6_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run deepseek-r1:32b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q6_K file (opens in a new tab)

huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q6_K

Original weights · Hugging Face

Full-precision files from DeepSeek, for fine-tuning, vLLM, Transformers.

huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from DeepSeek. The weights themselves download from Hugging Face.

github.com/deepseek-ai/DeepSeek-R1 (opens in a new tab)

git clone https://github.com/deepseek-ai/DeepSeek-R1

The R1 repo covers the distilled models too.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the C grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	32.1 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	3.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Total	54.8 / 100	54 ptsUsable (C) · 54 of 100 points: Model quality 32.1/45, Memory fit 4.0/25, Speed 3.7/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4070 SUPER

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	16.5 GB	12.0 GB	9.6 tok/s	Heavy offload	49 ptsUsable (C) · 49 of 100 points: Model quality 22.7/45, Memory fit 4.0/25, Speed 7.3/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q3_K_M`	19.4 GB	12.0 GB	6.1 tok/s	Heavy offload	52 ptsUsable (C) · 52 of 100 points: Model quality 27.2/45, Memory fit 4.0/25, Speed 5.8/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q4_K_M`	23.4 GB	12.0 GB	4.0 tok/s	Heavy offload	54 ptsUsable (C) · 54 of 100 points: Model quality 30.8/45, Memory fit 4.0/25, Speed 4.7/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q5_K_M`	26.6 GB	12.0 GB	3.0 tok/s	Heavy offload	54 ptsUsable (C) · 54 of 100 points: Model quality 31.4/45, Memory fit 4.0/25, Speed 4.1/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q6_K`	30.2 GB	12.0 GB	2.4 tok/s	Heavy offload	54 ptsUsable (C) · 54 of 100 points: Model quality 32.1/45, Memory fit 4.0/25, Speed 3.7/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q8_0`	38.1 GB	12.0 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`F16`	68.9 GB	12.0 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

86 ptsExcellent (S) · 86 of 100 points: Model quality 32.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q6_K · 46.5 tok/s
84 ptsGreat (A) · 84 of 100 points: Model quality 30.8/45, Memory fit 24.0/25, Speed 14.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q4_K_M · 37.7 tok/s
83 ptsGreat (A) · 83 of 100 points: Model quality 30.8/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q4_K_M · 35.0 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 30.8/45, Memory fit 24.0/25, Speed 13.2/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q4_K_M · 30.9 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 32.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 NVIDIA RTX PRO 6000 Blackwell Q6_K · 46.5 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 12.8/15 Apple M4 Max (64 GB) Q4_K_M · 17.6 tok/s

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.