Can RTX 4080 SUPER run LLaVA 1.6 7B?

Yes — runs locally

~78 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

Best quant

Q8_0

VRAM needed

8.5 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles LLaVA 1.6 7B comfortably using the Q8_0 quantization, which fits in 8.5 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Multimodal vision-language model. Understands images and answers questions about them.

Setup tutorial: LLaVA 1.6 7B on RTX 4080 SUPER

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

TL;DR

Run LLaVA 1.6 7B on your NVIDIA GeForce RTX 4080 SUPER with Ollama using the Q8_0 quantization. Expect Grade S performance at ~79 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) with CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at ~79 tok/sec, using 8.5GB of VRAM. The remaining 7.5GB of VRAM provides ample headroom for a context window of up to 3000 tokens, ensuring smooth and efficient operation.

1. Install runtimeOllama

curl -fsSL https://ollama.com/install.sh | sh
ollama install

2. Download the model

Download the Q8_0 quantized LLaVA 1.6 7B model (7.7GB file) from Hugging Face.

ollama pull mys/ggml_llava-v1.6-mistral-7b:Q8_0

3. Run it

ollama run mys/ggml_llava-v1.6-mistral-7b:Q8_0
ollama chat mys/ggml_llava-v1.6-mistral-7b:Q8_0

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention (--flash-attn) for faster inference. With 8.5GB VRAM used by the model, you will have approximately 7.5GB of VRAM left for context, allowing for a practical context window of around 3000 tokens.

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 is enabled with --flash-attn. If still slow, try reducing the batch size.

Model fails to load

Verify that the model file was downloaded correctly and that there are no disk space issues. Re-run the download command if necessary.

Alternative runtimes

Consider using LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for a lightweight alternative. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4080 SUPER.

Full LLaVA 1.6 7B details →

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FAQ (20)

What GPU do I need to run LLaVA 1.6 7B?

To run LLaVA 1.6 7B, you need a GPU with at least 5.0 GB of VRAM for the lowest quantization level, but 8.5 GB is recommended for better performance and higher quantization levels.

Is LLaVA 1.6 7B good for coding?

LLaVA 1.6 7B is primarily designed for multimodal tasks like understanding images and answering questions about them, so its capabilities for coding are limited compared to specialized coding models.

LLaVA 1.6 7B vs Llama 3.1 8B?

LLaVA 1.6 7B is a smaller, multimodal model with 7 billion parameters, while Llama 3.1 8B is a larger, text-only model with 8 billion parameters. LLaVA is better for image-related tasks, whereas Llama excels in text generation.

Can I run LLaVA 1.6 7B on a Mac?

Yes, you can run LLaVA 1.6 7B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM. M1 and M2 chips with Metal support are also viable options.

How much VRAM does LLaVA 1.6 7B need?

LLaVA 1.6 7B requires between 5.0 GB and 8.5 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM.

Is LLaVA 1.6 7B censored?

LLaVA 1.6 7B is not inherently censored, but it may include content filters to prevent harmful or inappropriate responses. The extent of these filters depends on the implementation and configuration.

Is LLaVA 1.6 7B commercial-use allowed?

Yes, LLaVA 1.6 7B is licensed under the Apache-2.0 license, which allows for commercial use as long as you comply with the terms of the license.

LLaVA 1.6 7B context length?

LLaVA 1.6 7B supports a context length of up to 4096 tokens, allowing for longer conversations and more detailed inputs.

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