Can RTX 5090 run DeepSeek R1 Distill 8B?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles DeepSeek R1 Distill 8B comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 114 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
The DeepSeek R1 Distill 8B model runs exceptionally well on the NVIDIA GeForce RTX 5090 with a Grade S performance, using the Q8_0 quantization, achieving ~154 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (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 approximately 154 tokens per second, using around 8.4GB of VRAM. This leaves you with 23.6GB of VRAM headroom, allowing for a practical context window of up to 131,072 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized model, which is an 8.0GB file from the Hugging Face repository.
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 32 --flash-attn --tensor-parallelism 24. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to leverage the multi-core architecture of the GPU.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers parameter to 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 4.
Model fails to load
Check that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
While Ollama is recommended for its ease of use and performance, you can also consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for lightweight deployment. Each alternative has its own strengths depending on your specific use case and preferences.
Other models that run great on RTX 5090
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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