Can RTX 3090 Ti run DeepSeek R1 Distill 8B?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

Best quant

Q8_0

VRAM needed

8.4 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles DeepSeek R1 Distill 8B comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 60 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 3090 Ti

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

TL;DR

Run DeepSeek R1 Distill 8B on an NVIDIA GeForce RTX 3090 Ti with Q8_0 quantization for Grade S performance at ~116 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 470 or higher, and CUDA 11.2 or higher installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 116 tokens per second, using around 8.4GB of VRAM. This leaves you with about 15.6GB of VRAM headroom, allowing for a practical context window of up to 131,072 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama init

2. 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.gguf

3. Run it

ollama run DeepSeek-R1-Distill-Llama-8B-Q8_0.gguf --n-gpu-layers 128 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 128 to utilize most of the GPU's memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across multiple cores. This configuration will allow you to achieve the best performance while keeping the VRAM usage around 8.4GB.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or decrease the batch size. Alternatively, try disabling --flash-attn if it is causing memory issues.

Slow inference speed

Ensure that CUDA and the NVIDIA drivers are up to date. Also, check if the --tensor-parallelism setting is correctly configured for your GPU.

Model fails to load

Verify the integrity of the downloaded model file and try re-downloading it. Ensure that the Ollama runtime is properly installed and initialized.

Alternative runtimes

While Ollama is the recommended runtime for this setup, you can also use LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for lightweight deployment scenarios. Choose the runtime based on your specific needs and preferences.

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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