Can RTX 4070 Ti SUPER run DeepSeek R1 Distill 8B?
Yes — runs locally
~70 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti 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 70 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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run DeepSeek R1 Distill 8B on an NVIDIA GeForce RTX 4070 Ti 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 approximately 77 tokens per second, using around 8.4GB of VRAM. The remaining 7.6GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context.
1. Install runtimeOllama
curl -L https://ollama.ai/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized version of DeepSeek R1 Distill 8B (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 --n-gpu-layers 32 --flash-attn
ollama chat DeepSeek-R1-Distill-Llama-8B-Q8_04. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster inference and better memory efficiency. With 8.4GB VRAM used by the model, you have 7.6GB of VRAM left for context, allowing for a practical context window of up to 131072 tokens.
Troubleshooting
Model runs out of VRAM during inference.
Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.
Inference is slow.
Ensure --flash-attn is enabled and update your NVIDIA drivers and CUDA to the latest versions.
Model fails to load.
Verify that the model file is downloaded correctly and that Ollama is installed properly. Try reinstalling Ollama or pulling the model again.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for lightweight deployment. Each runtime has its strengths, but Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4070 Ti SUPER.
Other models that run great on RTX 4070 Ti 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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