Can RTX 5090 run Llama 3.1 8B Instruct?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the FP16 quantization, which fits in 17.0 GB. Expected throughput is around 114 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 5090 with FP16 quantization for Grade S performance at ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 17GB 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 FP16 quantization, you can expect the model to run at approximately 77 tokens per second (tok/sec) with 17.0GB of VRAM in use. The remaining 15.0GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the FP16 quantized model (16.0GB file) from Hugging Face.
ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-f16.gguf3. Run it
ollama run --model Meta-Llama-3.1-8B-Instruct-f16.gguf --interactive
ollama chat --model Meta-Llama-3.1-8B-Instruct-f16.gguf4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the FP16 quantization. Set --n-gpu-layers to 32 to utilize the full GPU memory. Enable flash attention (--flash-attn) to speed up inference. With 17.0GB VRAM used by the model, you will have 15.0GB of VRAM left for context, allowing for a practical context window of up to 131072 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers (--n-gpu-layers) or decrease the context length.
Slow inference speed
Ensure that flash attention (--flash-attn) is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file is downloaded correctly and that there is sufficient disk space and VRAM available.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance settings, or Jan for a lightweight, easy-to-deploy solution. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 5090.
Other models that run great on RTX 5090
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.
Want personalized recommendations for your exact setup? Detect my hardware →