How much VRAM does DeepSeek R1 Distill Qwen 7B need?

DeepSeek R1 Distill Qwen 7B requires 6.6 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 4.4 GB.

What is the best GPU for DeepSeek R1 Distill Qwen 7B?

The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for DeepSeek R1 Distill Qwen 7B, achieving 360 tok/s at Q8_0 with an excellent rating.

What quantization should I use for DeepSeek R1 Distill Qwen 7B?

For the best quality, use Q6_K (6.6 GB VRAM). If your GPU has limited VRAM, Q4_K_M (4.4 GB) is the most efficient option with acceptable quality.

ReasoningChat7.62B

Reasoning

DeepSeek R1 Distill Qwen 7B

DeepSeek · MIT

Compact distilled reasoning model for chat and inference.

Parameters: 7.62B
Architecture: Dense
Context: 32,768 tokens
Released: 2025-01-20
Engines: llama.cpp, ollama, vLLM, TGI

Parameters

7.62B

VRAM

6.6 GB

Context

32K

Formats

GPUs

DeepSeek R1 Distill Qwen 7B (7.62B) requires 6.6 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 4.4 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 360 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.

Source: OwnRig methodology

VRAM (Recommended)

6.6 GB

Quantization

Q6_K

File Size

5.8 GB

Max Context

32K tokens

Primary Use

Reasoning

Memory

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	8.5 GB	7.5 GB
recommended	Q6_K	6.6 GB	5.8 GB
recommended	Q5_K_M	5.5 GB	4.8 GB
efficient	Q4_K_M	4.4 GB	3.8 GB

Scaling

Context Length Impact

KV cache VRAM at Q6_K quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	102 MB	6.7 GB
4K	307 MB	6.9 GB
8K	512 MB	7.1 GB
16K	1 GB	7.6 GB
32K	2 GB	8.6 GB

Compatible GPUs

22 devices


NVIDIA Grace Blackwell Ultra GB300	Q8_0	360 tok/s	Excellent
Apple M4 Max (36GB Unified)	Q4_K_M	52 tok/s	Excellent
NVIDIA GeForce RTX 3080 10GB	Q5_K_M	48 tok/s	Excellent
NVIDIA GeForce RTX 4070 Ti 12GB	Q5_K_M	48 tok/s	Excellent
NVIDIA GeForce RTX 4090	Q4_K_M	92 tok/s	Excellent
NVIDIA GeForce RTX 5080	Q8_0	85 tok/s	Excellent
NVIDIA RTX PRO 6000 Blackwell	Q8_0	129 tok/s	Excellent
NVIDIA RTX PRO 6000 Blackwell Max-Q	Q8_0	119 tok/s	Excellent
Apple M4 Pro (48GB)	Q8_0	38 tok/s	Good
NVIDIA GeForce RTX 3060 12GB	Q4_K_M	38 tok/s	Good
NVIDIA GeForce RTX 4060 8GB	Q4_K_M	32 tok/s	Good
NVIDIA RTX 4080 Laptop (120-150W)	Q5_K_M	34 tok/s	Good
AMD Radeon RX 7900 XTX	Q4_K_M	79 tok/s	Good
AMD Radeon Pro W7900	Q8_0	41 tok/s	Good
AMD Radeon RX 9070	Q8_0	150 tok/s	Good
AMD Radeon RX 9060 XT 16GB	Q8_0	75 tok/s	Good
NVIDIA GeForce RTX 5060 8GB	Q4_K_M	37 tok/s	Good
Apple M3 Pro (18GB Unified)	Q4_K_M	14 tok/s	Acceptable
NVIDIA RTX 4060 Laptop (40-60W)	Q4_K_M	19 tok/s	Acceptable
NVIDIA RTX 4070 Laptop (80-115W)	Q4_K_M	22 tok/s	Acceptable
AMD Radeon RX 7600	Q4_K_M	25 tok/s	Acceptable
AMD Radeon RX 9060 XT 8GB	Q8_0	–	Not viable

Showing 22 of 22 entries

FAQ

Frequently Asked Questions

How much VRAM does DeepSeek R1 Distill Qwen 7B need?: DeepSeek R1 Distill Qwen 7B requires 6.6 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 4.4 GB.
What is the best GPU for DeepSeek R1 Distill Qwen 7B?: The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for DeepSeek R1 Distill Qwen 7B, achieving 360 tok/s at Q8_0 with an excellent rating.
What quantization should I use for DeepSeek R1 Distill Qwen 7B?: For the best quality, use Q6_K (6.6 GB VRAM). If your GPU has limited VRAM, Q4_K_M (4.4 GB) is the most efficient option with acceptable quality.

Related Guides

Tutorial

The Complete Guide to Running LLMs Locally

Run large language models locally: hardware needs, Ollama and llama.cpp, model picks by use case, and quantization.

All models

Data confidence: estimated. Source

VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.DeepSeek is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.