Can RTX 5090 run Llama 3.2 1B Instruct?

Yes — runs locally

~216 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

32 GB

Model size

1.24B

Best quant

FP16

VRAM needed

2.8 GB

The verdict

The RTX 5090 (32 GB VRAM) handles Llama 3.2 1B Instruct comfortably using the FP16 quantization, which fits in 2.8 GB. Expected throughput is around 216 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Ultra-compact 1B model. Runs on virtually any device including smartphones.

Setup tutorial: Llama 3.2 1B Instruct on RTX 5090

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

TL;DR

Llama 3.2 1B Instruct runs at Grade S on the NVIDIA GeForce RTX 5090 with FP16 quantization, achieving ~657 tok/sec.

Prerequisites

Before starting, ensure you have at least 3GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA driver (version 525.60.12 or later), and CUDA 11.8 installed.

Expected performance

With the FP16 quantization, you can expect the model to run at approximately 657 tokens per second, consuming about 2.8GB of VRAM. Given the 32GB VRAM of the RTX 5090, you will have 29.2GB of VRAM available for context, allowing you to achieve a practical context window of up to 131,072 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the FP16 quantized model (2.3GB file) from Hugging Face.

ollama pull bartowski/Llama-3.2-1B-Instruct-GGUF:Llama-3.2-1B-Instruct-f16.gguf

3. Run it

ollama run Llama-3.2-1B-Instruct-f16.gguf --interactive
ollama chat Llama-3.2-1B-Instruct-f16.gguf

4. Optimize for RTX 5090

For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the FP16 quantization and set --n-gpu-layers to 128 to fully utilize the GPU. Enable flash attention (--flash-attn) for faster inference and consider using tensor parallelism (--tensor-parallel-size 2) if you need to scale further. This configuration will allow you to achieve the target ~657 tok/sec while keeping the VRAM usage around 2.8GB, leaving ample headroom for larger context windows.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers (--n-gpu-layers 64) or disable flash attention (--no-flash-attn).

Low token generation speed

Ensure that CUDA is properly installed and update your NVIDIA drivers to the latest version.

Model fails to load

Verify the integrity of the downloaded model file and try downloading it again.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the inference process or specific features not supported by Ollama. For example, LM Studio offers a graphical interface and advanced tuning options, while llama.cpp provides a lightweight, highly customizable solution. Jan is suitable for deployment scenarios requiring low latency and high throughput.

Full Llama 3.2 1B Instruct details →

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FAQ (20)

What GPU do I need to run Llama 3.2 1B Instruct?

To run Llama 3.2 1B Instruct, you need a GPU with at least 1.3 GB of VRAM, but 2.8 GB is recommended for better performance, especially with higher quantization levels.

Is Llama 3.2 1B Instruct good for coding?

Llama 3.2 1B Instruct is suitable for basic coding tasks and can provide useful suggestions, but its smaller size may limit its effectiveness for more complex programming scenarios compared to larger models.

Llama 3.2 1B Instruct vs Llama 3.1 8B?

Llama 3.2 1B Instruct is more compact and runs on lower-end hardware, while Llama 3.1 8B offers better performance and accuracy due to its larger size, making it more suitable for demanding tasks.

Can I run Llama 3.2 1B Instruct on a Mac?

Yes, Llama 3.2 1B Instruct can run on Macs, provided your Mac has a compatible GPU with at least 1.3 GB of VRAM or sufficient CPU resources.

How much VRAM does Llama 3.2 1B Instruct need?

Llama 3.2 1B Instruct requires between 1.3 GB and 2.8 GB of VRAM, depending on the quantization level used.

Is Llama 3.2 1B Instruct censored?

Llama 3.2 1B Instruct is not inherently censored, but it adheres to ethical guidelines and may filter out inappropriate content based on its training data and configuration.

Is Llama 3.2 1B Instruct commercial-use allowed?

Yes, Llama 3.2 1B Instruct is licensed under the llama3.2 license, which allows for commercial use as long as you comply with the terms of the license.

Llama 3.2 1B Instruct context length?

Llama 3.2 1B Instruct supports a context length of up to 131,072 tokens, allowing for extensive input and output sequences.

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