Can I run DeepSeek-R1-Distill-Qwen-7B on a NVIDIA RTX 4060?

Yes.Runs

You can run this with Q4 quantization. On a NVIDIA RTX 4060 (8 GB VRAM), DeepSeek-R1-Distill-Qwen-7B needs about 6.0 GB (Q4) and should generate at roughly ~42 tok/s. Recommended: Run on your GPU with Q4.

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Why

Full precision needs ~19 GB which exceeds your accelerator, but 4-bit (Q4_K_M / INT4) needs only ~6.0 GB and fits.

How to run DeepSeek-R1-Distill-Qwen-7B on a RTX 4060

Run with Ollama (easiest)

ollama run deepseek-r1:7b

Or with llama.cpp

llama-cli -hf deepseek-ai/DeepSeek-R1-Distill-Qwen-7B -p "Hello"

Serve with vLLM (OpenAI-compatible API)

# Fast production serving on http://localhost:8000/v1
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Python (Transformers)

from transformers import pipeline, BitsAndBytesConfig
# pip install transformers accelerate bitsandbytes
quant = BitsAndBytesConfig(load_in_4bit=True)
pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", device_map="auto",
                model_kwargs={"quantization_config": quant})
print(pipe("Hello", max_new_tokens=50))

Want it to run comfortably at higher precision? The smallest GPU that runs DeepSeek-R1-Distill-Qwen-7B well is the NVIDIA RTX 3080 (10GB) (10 GB).

Try the full analyzer

DeepSeek-R1-Distill-Qwen-7B on other GPUs

RTX 509032 GB VRAM RTX 508016 GB VRAM RTX 5070 Ti16 GB VRAM RTX 507012 GB VRAM RTX 409024 GB VRAM RTX 4080 Super16 GB VRAM

Other models on a RTX 4060

Qwen2.5-0.5Bon RTX 4060 GPT-2on RTX 4060 Phi-3-minion RTX 4060 Qwen2.5-Coderon RTX 4060 Mistral-7Bon RTX 4060 Llama-3.1-8Bon RTX 4060