Can RTX 3080 Ti run DeepSeek R1 Distill 8B?

Yes — runs locally

~46 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

Best quant

Q5_K_M

VRAM needed

5.8 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles DeepSeek R1 Distill 8B comfortably using the Q5_K_M quantization, which fits in 5.8 GB. Expected throughput is around 46 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Compact reasoning model. Good reasoning capabilities in a small package.

Setup tutorial: DeepSeek R1 Distill 8B on RTX 3080 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

The DeepSeek R1 Distill 8B model runs exceptionally well on an NVIDIA GeForce RTX 3080 Ti with a grade S performance, using the Q5_K_M quantization, achieving around 84 tokens per second.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the Q5_K_M quantization, you can expect the model to run at approximately 84 tokens per second, using around 5.8GB of VRAM. This leaves you with 6.2GB of VRAM for context, enabling a practical context window of around 100,000 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_K_M quantized version of the DeepSeek R1 Distill 8B model, which is approximately 5.3GB in size.

ollama pull bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF:DeepSeek-R1-Distill-Llama-8B-Q5_K_M.gguf

3. Run it

ollama run DeepSeek-R1-Distill-Llama-8B-Q5_K_M --n-gpu-layers 32 --flash-attn --context-length 131072

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory without exceeding it. Enable --flash-attn to speed up attention computations. Given the 5.8GB VRAM usage, you will have about 6.2GB of VRAM left for context, allowing for a practical context window of around 100,000 tokens.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the context length using --context-length.

Slow inference speed

Ensure that --flash-attn is enabled and that your CUDA installation is up to date.

Model not loading

Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio provides a more user-friendly interface but may require more system resources. llama.cpp is highly optimized for low-memory systems but lacks some features. Jan is another lightweight option suitable for quick prototyping. Choose based on your specific needs and system constraints.

Full DeepSeek R1 Distill 8B details →

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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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