Can RTX 4080 SUPER run DeepSeek R1 Distill 8B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles DeepSeek R1 Distill 8B comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Compact reasoning model. Good reasoning capabilities in a small package.
Setup tutorial: DeepSeek R1 Distill 8B on RTX 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run DeepSeek R1 Distill 8B on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at ~77 tok/sec, using approximately 8.4GB of VRAM. This leaves about 7.6GB of VRAM for context, allowing for a practical context window of around 131,072 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized model (8.0GB file) from Hugging Face.
ollama pull bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF:DeepSeek-R1-Distill-Llama-8B-Q8_0.gguf3. Run it
ollama run DeepSeek-R1-Distill-Llama-8B-Q8_0.gguf --n-gpu-layers 48 --flash-attn
ollama chat DeepSeek-R1-Distill-Llama-8B-Q8_0.gguf4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 48 to utilize most of the GPU memory while leaving enough headroom for context. Enable --flash-attn to speed up attention computations. With these settings, you should achieve ~77 tok/sec.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 32 or enable --cpu-offload to offload some layers to CPU.
Slow inference speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model not found
Verify the model path and ensure it matches the downloaded file name exactly.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a GUI interface. llama.cpp offers more fine-grained control over model parameters and is ideal for advanced users. Jan is a lightweight runtime that is easy to set up but may lack some features compared to Ollama. Choose based on your specific needs and comfort level with command-line tools.
Other models that run great on RTX 4080 SUPER
FAQ (20)
What GPU do I need to run DeepSeek R1 Distill 8B?
To run DeepSeek R1 Distill 8B, you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 8.4 GB for the highest. NVIDIA GPUs like the RTX 3060 or higher are recommended.
Is DeepSeek R1 Distill 8B good for coding?
DeepSeek R1 Distill 8B is well-suited for coding tasks due to its strong reasoning capabilities and compact size, making it efficient for code generation and debugging.
DeepSeek R1 Distill 8B vs Llama 3.1 8B?
DeepSeek R1 Distill 8B offers better reasoning capabilities in a smaller package compared to Llama 3.1 8B, which may have a larger context length but is generally less efficient in terms of resource usage.
Can I run DeepSeek R1 Distill 8B on a Mac?
Yes, you can run DeepSeek R1 Distill 8B on a Mac with an M1 or M2 chip, but performance will be better on a Mac with a dedicated GPU like the RTX 3060 or higher.
How much VRAM does DeepSeek R1 Distill 8B need?
DeepSeek R1 Distill 8B requires between 5.1 GB and 8.4 GB of VRAM, depending on the quantization level used.
Is DeepSeek R1 Distill 8B censored?
DeepSeek R1 Distill 8B is not inherently censored, but it adheres to ethical guidelines and may filter out inappropriate content based on the training data and configuration settings.
Is DeepSeek R1 Distill 8B commercial-use allowed?
Yes, DeepSeek R1 Distill 8B is licensed under the MIT License, which allows for commercial use without restrictions.
DeepSeek R1 Distill 8B context length?
DeepSeek R1 Distill 8B has a context length of 131,072 tokens, allowing it to handle very long sequences of text.
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