Can RTX 4060 Ti 16GB run Llama 3.1 8B Instruct?

Yes — runs locally

~46 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

16 GB

Model size

Best quant

Q8_0

VRAM needed

8.4 GB

The verdict

The RTX 4060 Ti 16GB (16 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q8_0 quantization, which fits in 8.4 GB. Expected throughput is around 46 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 4060 Ti 16GB

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

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the Q8_0 quantization for ~77 tok/sec.

Prerequisites

Before starting, ensure you have at least 16GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.13 or later) installed. You also need CUDA 11.8 or later.

Expected performance

With the Q8_0 quantization, you can expect ~77 tok/sec performance, using approximately 8.4GB of VRAM. This leaves 7.6GB of VRAM for context, allowing for a practical context window of around 131072 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. 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.gguf

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat Meta-Llama-3.1-8B-Instruct-Q8_0

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 32 to utilize the 16GB VRAM efficiently. Enable --flash-attn to speed up attention computations. Given the 16GB VRAM, you can achieve a practical context window of around 131072 tokens with 8.4GB VRAM in use, leaving 7.6GB for context.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.

Slow token generation

Ensure --flash-attn is enabled to optimize attention computations.

Model not loading

Check if the model file is fully downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly GUI, llama.cpp for low-level customization, and Jan for distributed inference across multiple GPUs. Ollama is recommended for its ease of use and efficient performance on single GPUs like the RTX 4060 Ti 16GB.

Full Llama 3.1 8B Instruct details →

Other models that run great on RTX 4060 Ti 16GB

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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