Llama-3.2-11B

[onyx wing]

To deploy your fine-tuned unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit on an RTX 4090 for fast inference, use Unsloth's FastVisionModel with 4-bit loading. This enables low VRAM usage and 2x faster inference. Example:

from unsloth import FastVisionModel
model, tokenizer = FastVisionModel.from_pretrained(
    "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
    load_in_4bit=True,
)
FastVisionModel.for_inference(model)

This setup is optimized for your GPU and requires no extra dependencies. For even faster inference, consider using vLLM with Unsloth by setting fast_inference=True if you need high throughput batch serving. See Unsloth Inference Guide and Vision Fine-tuning Notebooks for more.

Would you like step-by-step deployment or API serving details?

Sources:

• Unsloth Inference Guide

💡 **Hint:** Mention @RunLLM in the thread for followups.

[livid plank]

okay I have set it and I have my merged model what I need to do next. Deploy on vllm?

[onyx wing]

Yes, to deploy your merged model on vLLM for fast inference, install vLLM and serve the model using the vllm serve command. Example:

pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
vllm serve /path/to/your/merged-model --tensor-parallel-size 1

This will start an OpenAI-compatible API server on your RTX 4090. You can then send requests using the OpenAI API or Python client. For more, see Unsloth Vision Notebook.

Would you like details on API usage or troubleshooting?

Sources:

• https://huggingface.co/unsloth/Magistral-Small-2506#inference
• https://github.com/unslothai/unsloth/issues/2516

💡 **Hint:** Mention @RunLLM in the thread for followups.

[livid plank]

Is it normal that I can't run on 24gb gpu this model on vllm: Llama-3.2-11B-Vision-Instruct-bnb-4bit ?

[onyx wing]

It seems likely that running unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit on vLLM with a 24GB GPU (like RTX 4090) may not work due to high VRAM requirements, especially for vision models, even with 4-bit quantization. Users have reported OOM (out of memory) errors on similar setups, and Unsloth recommends at least 24GB VRAM but actual usage can exceed this depending on context length and batch size. This is a common limitation for large vision models on consumer GPUs[^1][^2].

Would you like tips for reducing memory usage or alternative deployment strategies?

Sources:

• https://github.com/unslothai/unsloth/issues/2626
• https://github.com/unslothai/unsloth/issues/2366

💡 **Hint:** Mention @RunLLM in the thread for followups.

[livid plank]

Did people report for this model: Qwen2.5-VL-7B-Instruct-bnb-4bit with 24gb ram?

[onyx wing]

Yes, users have reported running Qwen2.5-VL-7B-Instruct-bnb-4bit on 24GB GPUs (like RTX 3090/4090), but many encounter out-of-memory (OOM) errors, especially during inference or fine-tuning, even with 4-bit quantization. This is a known issue for vision models due to their higher memory requirements compared to text-only models. See this issue for details.

Would you like suggestions to reduce memory usage or workarounds?

Sources:

• https://github.com/unslothai/unsloth/issues/2064

💡 **Hint:** Mention @RunLLM in the thread for followups.

[livid plank]

and with Qwen2.5-VL-7B-Instruct_GGUF Q4_0?