Can RTX 3070 Ti run Llama 3.1 8B Instruct?

Yes — runs locally

~34 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

8 GB

Model size

Best quant

Q4_K_M

VRAM needed

5.1 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the Q4_K_M quantization, which fits in 5.1 GB. Expected throughput is around 34 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 3070 Ti

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

TL;DR

Run Llama 3.1 8B Instruct on an NVIDIA GeForce RTX 3070 Ti with Ollama using the Q4_K_M quantization. Expect Grade S performance at ~64 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 510.47.03 or later), and CUDA 11.2 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 64 tokens per second, using about 5.1GB of VRAM. The remaining 2.9GB of VRAM will provide a practical context window of up to 131072 tokens, depending on the complexity of the input.

1. Install runtimeOllama

curl -L https://ollama.com/install.sh | bash
ollama install

2. Download the model

Download the Q4_K_M quantized model (4.6GB) from Hugging Face.

ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf --n-gpu-layers 12 --flash-attn
ollama chat Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers 12 flag to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. This configuration should allow you to achieve ~64 tok/sec while keeping VRAM usage around 5.1GB, leaving 2.9GB for context.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 8 or lower.

Slow inference speed

Ensure flash attention is enabled with --flash-attn and update your NVIDIA drivers to the latest version.

Model not found

Verify the model file path and ensure it matches the name used in the ollama pull command.

Alternative runtimes

Consider using LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for a lightweight alternative. Choose based on your specific needs, such as ease of use, performance tuning, or resource constraints.

Full Llama 3.1 8B Instruct details →

Other models that run great on RTX 3070 Ti

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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