Can RTX 4090 run Llama 3.1 8B Instruct?
Yes — runs locally
~96 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 96 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Meta's 8B parameter instruction-tuned model. Great balance of performance and efficiency for local deployment.
Setup tutorial: Llama 3.1 8B Instruct on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Llama 3.1 8B Instruct runs at Grade S on an NVIDIA GeForce RTX 4090 with Q8_0 quantization, achieving ~116 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.13 or later) installed. Additionally, CUDA 11.8 or later is required for optimal performance.
Expected performance
With the Q8_0 quantization, you can expect the model to run at approximately 116 tokens per second, using around 8.4GB of VRAM. Given the remaining 15.6GB of VRAM, you can achieve a practical context window of up to 131,072 tokens, making it suitable for long-form text generation and complex tasks.
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/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-Q8_0.gguf3. Run it
ollama run Meta-Llama-3.1-8B-Instruct-Q8_0 --interactive
ollama chat Meta-Llama-3.1-8B-Instruct-Q8_04. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the GPU's 24GB VRAM efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 8.4GB VRAM used by the model, you have 15.6GB of VRAM left for context, allowing for a large practical context window.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 24 or 16.
Slow inference speed
Ensure that flash attention (--flash-attn) is enabled and that the CUDA toolkit is properly installed and up to date.
Model fails to load
Verify that the model file has been downloaded correctly and that the file path is correct. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a user-friendly GUI, while llama.cpp offers more fine-grained control over model parameters. Jan is another lightweight option that can be useful for quick prototyping. Choose an alternative runtime based on your specific needs, such as ease of use or customization options.
Other models that run great on RTX 4090
FAQ (20)
What GPU do I need to run Llama 3.1 8B Instruct?
To run Llama 3.1 8B Instruct, you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 17.0 GB for full precision.
Is Llama 3.1 8B Instruct good for coding?
Llama 3.1 8B Instruct is well-suited for coding tasks, offering a good balance of performance and efficiency for generating code and providing programming assistance.
Llama 3.1 8B Instruct vs Llama 3.1 8B?
Llama 3.1 8B Instruct is an instruction-tuned version of Llama 3.1 8B, making it better suited for following user instructions and generating more coherent and contextually relevant responses.
Can I run Llama 3.1 8B Instruct on a Mac?
Yes, you can run Llama 3.1 8B Instruct on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.
How much VRAM does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct requires between 5.1 GB and 17.0 GB of VRAM, depending on the quantization level used.
Is Llama 3.1 8B Instruct censored?
Llama 3.1 8B Instruct is not inherently censored, but it may include content filters to prevent harmful or inappropriate outputs.
Is Llama 3.1 8B Instruct commercial-use allowed?
Llama 3.1 8B Instruct is licensed under the llama3.1 license, which allows for commercial use, but you should review the specific terms to ensure compliance.
Llama 3.1 8B Instruct context length?
Llama 3.1 8B Instruct has a context length of 131,072 tokens, allowing it to handle very long sequences of text.
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