The advent of LLaVA (Large Language and Vision Assistant) has brought multimodal AI – the combination of vision and language – within reach of individual developers and enthusiasts. In this article, we’ll show you how to run LLaVA 1.7.1 on RunPod, enabling a single pod to handle both image and text understanding. You’ll learn what LLaVA is capable of, why the latest 1.7.1 version is exciting, and how to deploy and use it on RunPod’s cloud platform. By the end, you can ask an AI not just to write answers, but to see images and respond – all in one convenient setup. (New to RunPod? Create an account to get your GPU pod ready in minutes and follow along.)

What is LLaVA? LLaVA stands for Large Language-and-Vision Assistant. It’s an open-source multimodal model that combines a powerful vision encoder (like CLIP ViT) with a large language model (such as Vicuna, based on LLaMA). In simpler terms, LLaVA can take an image as input and have a conversation about it – describing what’s in the image, answering questions about the image’s content, and so on. It was introduced in 2023 as a research project (with LLaVA 1.5 being a notable version that achieved strong results). Think of LLaVA as a step toward an open-source version of GPT-4’s multimodal capabilities. Given an image and a prompt, it can output a detailed answer, reasoning about the visual elements. The 1.7.1 version of LLaVA represents the latest improvements from the community: refinements in the training, bug fixes, and compatibility with newer models. In fact, LLaVA’s authors showed that it can reach about 85% of GPT-4’s performance on certain vision-language tasks – an impressive feat for an open model!

By running LLaVA on RunPod, you get to leverage GPU acceleration for both the image and text processing parts. This means you can do things like: upload a picture and ask “What is happening in this image?”, and get a coherent answer, all via API or web interface. Let’s dive into how to set this up.

How Do I Deploy the LLaVA 1.7.1 Vision-Language Model on RunPod?

Deploying LLaVA on RunPod is very similar to deploying other AI frameworks, with the added step that we need a template supporting vision inputs. Luckily, the community (and RunPod team) have created a ready-to-go LLaVA 1.7 template for us. Here’s how to get it running:

1. Find the LLaVA Template: Log in to RunPod and go to the Explore page. Search for “LLaVA” – you should see a template named along the lines of “LLaVA 1.7.x” or simply LLaVA with a version number. The current one to use is LLaVA 1.7.1 (as that’s the latest image updated, roughly 3 weeks ago at the time of writing). This template was built to work on RunPod and includes all necessary pieces (the vision encoder, LLM, and server). Click Deploy on this template. (If you are unable to find it by search, it might be under Community Templates; look for the description mentioning LLaVA or multimodal. As a hint, the template is based on a Docker image maintained by runpod community developers.)
2. Select GPU and Specs: LLaVA is heavier than text-only models because it runs both an image analysis model and a large language model. Here’s what to consider when choosing resources:
   • Model sizes: LLaVA typically comes in a 7B variant (Vicuna 7B backbone) and a 13B variant (Vicuna 13B). There are even larger ones (like a 34B), but those are rare and extremely demanding. The default in most templates is a 7B model (often LLaVA 1.5 or 1.6 with Vicuna-7B). A 7B LLaVA model in float16 will require around 14–16 GB of GPU memory for the language part, plus a couple GB for the vision encoder (CLIP) and overhead. So a 16 GB GPU is about the minimum for 7B, and 24 GB gives some safety margin.
   • For 13B LLaVA, you’ll need roughly double that. The developers recommend at least a 48 GB GPU (like an NVIDIA A6000 or better) for the 13B model. In fact, if you only have a 24 GB card, the 13B model likely won’t load in full precision – you’d need to use 8-bit loading techniques or run out-of-core which the container might not support well. Therefore, if you plan on 13B, choose an A6000 (48 GB) or A100 40 GB at a minimum (40 GB might work if optimizations are in place, but 48 GB is safer).
   • GPUs with multi-GPU option: The LLaVA RunPod template might allow adding multiple GPUs to the pod. However, be cautious – not all code automatically utilizes multiple GPUs unless configured. LLaVA’s backend can sometimes use DeepSpeed or model parallelism if set up. The template info should mention if multi-GPU is supported. If it is, you could, for instance, deploy 2×A6000 to get effectively 96 GB and run a huge model or multiple instances. For most users, start with a single GPU (it’s simpler).
   • Region and volume: Pick a region close to you for faster image upload/download. And ensure the pod has enough disk space. Images aren’t huge, but the model weights are. The 7B model weights could be 10–20 GB (especially with vision), and if you use 13B, could be 20–40 GB. The default container disk on RunPod is often 125 GB ephemeral, which is plenty. No need for an extra volume unless you want to save outputs or have persistence (not required for a quick setup).
3. Environment Variables (Model selection): The LLaVA template may have environment variables similar to KoboldCPP’s. For example, an env var named MODEL might let you choose which LLaVA model variant to load. Check the template description. If nothing is set, it probably defaults to a certain model (commonly the maintainer might have set it to LLaVA 1.6 Mistral-7B as default, as indicated in the Docker readme). If there is a MODEL field and you know a specific checkpoint you want, you can put the Hugging Face path. Otherwise, stick with the default for now – you can always change it later. The template will handle downloading the model weights.
4. Deploy the Pod: Click Deploy and wait for the pod to initialize. It will download the LLaVA Docker image (which is quite large, ~20 GB+ because it includes the environment and maybe a default model) and then download the model weights (if not already baked into the image). This could take a few minutes, so be patient. Once the pod status is Running, we’re ready to connect.
5. Connect to LLaVA’s Interface: The LLaVA template runs a couple of services. Based on the documentation, it likely runs:
   • A web server for the LLaVA chat interface (probably a web UI on a certain port).
   • Possibly Jupyter Lab (on port 8888) for an interactive notebook environment.
   • Possibly a developer code server (like VSCode via CodeServer on port 7777) – some templates include this for convenience.
   • Maybe a file uploader on port 2999 (as noted by the template ports).
   • And importantly, LLaVA’s API or chat server on a port (in one implementation, the chat interface might be on port 3001 internally, mapped to 3000 externally).
   In the RunPod Connect tab, you’ll see the list of ports you can open. Look for one that is likely the main interface, perhaps labeled by the template. For example, if you see ports “3000 -> 3001 (LLaVA)” and “8888 -> 8888 (Jupyter)”, you know that port 3000 is the one to open for the LLaVA web UI. Click Connect via HTTP on port 3000. This should open a browser tab with the LLaVA user interface or API documentation.What does the LLaVA interface look like? Depending on the template, it could be:
   • A simple web page with an upload button for an image and a text box for your question.
   • Or it might not have a fancy UI at all, and instead expects you to use Jupyter or an API client. If it’s not obvious, check the pod logs or any output on the RunPod dashboard – the template might print a message like “LLaVA server running at http://0.0.0.0:3001” or similar. In some cases, you might use the Jupyter notebook provided to interact with LLaVA (there could be example notebooks preloaded).
   Most likely, the template by Ashley (who built LLaVA Docker) includes a basic Gradio interface. Let’s assume you have a UI: you should see an option to upload an image and a chat-style interface to ask questions. If so, go ahead and test it:
   • Upload a picture (something simple, like a photo of a dog or a meme).
   • In the text box, ask a question or give an instruction about the image, e.g., “Describe this image” or “What is the dog doing in this picture?”.
   • Hit submit/generate. LLaVA will process the image through its vision encoder and generate a text answer via the language model. This may take a few seconds, especially the first time as the model warms up.
   • You should then see a response appear, describing the image or answering the question!
   If there’s no graphical UI, you might need to rely on the Jupyter notebook: likely there would be a notebook with examples. In that case, open the Jupyter link (port 8888), log in if a password is required (some templates set a default Jupyter password, check env var or logs – often it’s blank or “runpod”). Once in Jupyter, look for a provided .ipynb file (maybe named “llava_demo.ipynb” or similar). That notebook would have cells to upload an image (or specify an image URL) and then a cell to send a query to the model. You can run those cells to interact with LLaVA. While this approach works, it’s a bit less user-friendly than a Gradio UI, so hopefully the template provides the latter.
6. Using the LLaVA API (Optional): If the template is running an API server (which LLaVA can do via a Flask API as per the GitHub instructions), you could use the pod’s proxy URL to send HTTP requests. For example, the template might instruct that once running, you can do a POST request to http://<pod-id>.runpod.net:5000 with an image and prompt to get a JSON response. This is more advanced and typically for integrating LLaVA into other applications. For now, if you have the web UI working, you might not need the API directly. But it’s good to know it’s possible. You could even build your own front-end or script that hits the LLaVA pod’s endpoint – enabling you to, say, programmatically analyze images. For details on the API endpoints, you might refer to the [LLaVA GitHub wiki] or any documentation printed in the RunPod template notes.

And that’s it – you have LLaVA 1.7.1 running on a RunPod GPU! You can now have a conversation about images. This opens up a world of possibilities:

• Visual Chatbot: You can ask it to describe photos, interpret charts, or help you understand diagrams.
• Image QA: You provide an image and ask specific questions (“How many people are in this photo?”, “What does the text in this sign say?” – note: LLaVA can do basic OCR on clear text in images, though it’s not as strong as dedicated OCR).
• Creative Applications: Give it a piece of art and ask for a story about it, or show a meme and ask why it’s funny (LLaVA might surprise you).
• Testing Multimodal Models: Compare LLaVA’s responses to those from captioning models like BLIP-2 or to the closed-source GPT-4 on the same image, to gauge how far open models have come.

It’s worth noting that LLaVA 1.7.1 being the latest means it likely includes improved alignment or more data (LLaVA 1.5 was a big jump; 1.7 might incorporate newer fine-tuning or support for newer base models like Mistral). The Docker we used is tailored for RunPod, meaning the common hassles (like installing system libraries for image processing, setting up ports) are taken care of. RunPod’s template system shines here – it turned a complicated setup into a one-click deploy. As the LLaVA GitHub states, the aim is “end-to-end large multimodal model” capability, and that’s exactly what you have at your fingertips now.

Internal Links and Multimodal AI Context

• For a deeper understanding of LLaVA’s architecture and performance, you might want to read the official [LLaVA project page] . It discusses how the model was trained (using GPT-4 generated data to teach the vision+language model) and some of its benchmark achievements. It’s quite technical, but provides insight into how LLaVA achieves its GPT-4-like abilities.
• RunPod’s blog doesn’t yet have a dedicated LLaVA article (aside from this one!), but we have related content on multimodal AI. For instance, our case study on [AnonAI’s platform] mentions how they leveraged vision models alongside language models to handle image prompts securely. It’s an example of real-world use of multimodal AI on RunPod.
• You may also be interested in other ways to serve models that require special inference tricks. RunPod offers a template for [vLLM serverless inference] which is optimized for language tasks, and while that’s not multimodal, it shows the range of options (from high-throughput text serving to flexible multimodal pods like LLaVA). If you’re curious, see “Run LLMs on RunPod Serverless with vLLM” (a guide in our blog) for how pure text models can be scaled differently.
• If you’d like to experiment with other vision+language models, there are a few: e.g., BLIP-2, MiniGPT-4, ImageBind etc. RunPod might not have one-click templates for all, but you can often run them in our environment with some setup. LLaVA is one of the most popular because of its strong performance and open license.

Now onto some frequently asked questions that come up when using LLaVA:

FAQ

Q: What kinds of questions can LLaVA answer about images?

A: LLaVA can handle a range of visual question-answering (VQA) and description tasks. It can describe the content of an image (objects, scenery, people’s appearances), interpret simple actions (“The dog is playing fetch with a ball”), and answer specific queries (“What color is the car?”, “Is this person happy or sad in the photo?”). It can also handle more complex reasoning to an extent (“What might happen next in this image?” or “Why is this meme funny?”) – though keep in mind it’s not perfect and might miss subtleties. LLaVA was trained on GPT-4 generated descriptions and conversations about images , so it tries to mimic that kind of response. It’s quite good at everyday images and common objects. However, it might struggle with very abstract images or require very detailed analysis (for example, reading a paragraph of dense text from an image is not its forte; a dedicated OCR model would do better). Treat LLaVA as a general-purpose vision assistant – very useful, but not infallible.

Q: How does LLaVA differ from something like OpenAI’s Vision (GPT-4 with vision)?

A: The concept is similar – both take images + text and output text. The difference is in the models and availability. GPT-4 with vision is a closed model (you can’t self-host it, and it’s only via API with certain constraints). LLaVA is open-source; you can run it on your own hardware (as we do on RunPod). In terms of performance, GPT-4 Vision is stronger and more reliable in many cases (it has seen more data and has a larger architecture). LLaVA is catching up in specific domains – for instance, LLaVA 1.5/1.7 is quite good at describing images and even scored 85.1% as well as GPT-4 in one internal evaluation. But expect that GPT-4 might still win on very complex reasoning or obscure images. Another difference: LLaVA’s knowledge is limited by its training (which cut off around mid-2023 data and whatever images it saw). If you show it a very recent event photo or a celebrity it doesn’t know, it might falter. GPT-4 might have updated knowledge or better reasoning in such cases. Nonetheless, LLaVA is continually improving, and the fact that you can run it yourself is a huge advantage for privacy and customization.

Q: What base models does LLaVA use? Can I choose which one to use?

A: LLaVA is more like a framework – it needs two components: a vision encoder (usually CLIP ViT-L/14 is used) and a language model (like Vicuna, which is a fine-tuned LLaMA). Different LLaVA variants use different language backbones:

• The original LLaVA used Vicuna-13B and Vicuna-7B as backbones (Vicuna is a fine-tuned LLaMA trained on conversation data).
• Later variants have tried other models like Mistral 7B or even LLaMA-2. For example, the Docker template’s default was liuhaotian/llava-v1.6-mistral-7b which indicates a LLaVA 1.6 model using a Mistral 7B LLM. Mistral is another LLM that can offer strong performance for its size.
• There are also larger versions like a 34B (one listed is LLaVA-1.6 “Hermes-Yi-34B”), but those are not commonly run due to their size.
• When deploying, if the template or MODEL env allows, you can pick which one. If not explicitly, it’s using the default included model. To change it, you might need to manually adjust the environment and possibly the code (which is not trivial unless you know what you’re doing). In short, unless you have a specific need, stick with the default model that comes with the template to ensure compatibility. If you do experiment, know that the Hugging Face Hub has repositories like llava-v1.5-7b, llava-v1.5-13b, llava-v1.6-* etc. You could try pointing the container to those, as long as the model is supported. Always match the vision encoder requirement – most use CLIP ViT-L/14, which is standard and included.

Q: My LLaVA pod is running slow or running out of memory. What can I do?

A: Multimodal models are heavy. If it’s slow:

• Ensure you’re using a strong enough GPU. If you chose a lower-tier GPU (say a T4 or an older card), it will be significantly slower. Tensor cores on newer GPUs (like A-series or RTX 3000/4000 series) make a big difference for these models. For decent performance, GPUs like 3090, 4090, A100 are recommended.
• Check if the model is running in a lower precision. Some LLaVA containers might automatically use FP16 (which they should). If by chance it’s using FP32, that doubles memory use and slows things down. Using something like bitsandbytes for 8-bit could help memory, but might not be in use by default.
• If you’re running out of memory (errors about CUDA out of memory):
  • Try a smaller model (7B instead of 13B).
  • Or if you must use 13B, consider switching to an 8-bit loading if available. The LLaVA repository doesn’t natively mention 8-bit, but you could try to install and use bitsandbytes if you are comfortable editing the environment.
  • The easiest though: upgrade to a larger VRAM GPU on RunPod. It’s what we pay for, but it solves the issue quickly. For instance, if you were on a 24 GB card and getting OOM for 13B, moving to a 40 GB or 48 GB will likely fix it.
• Also, avoid loading extremely high-res images or too many images at once. Typically LLaVA preprocesses images to a certain resolution (like 224x224 or 336x336 pixels for CLIP). One image at a time is the standard use; feeding it multiple images in one go might not be supported unless the code is changed for that.

Q: Are there any privacy or safety considerations when using LLaVA?

A: One big reason people use self-hosted models like LLaVA is privacy. The images and data you send to your RunPod LLaVA pod are not seen by any third-party (RunPod doesn’t inspect your data , and the model is running in your isolated pod). This is unlike using a service like OpenAI’s API where you might send images to them and have to trust their handling. However, note a few things:

• If your RunPod pod is running with a proxy URL exposed, anyone who somehow knows your pod’s URL and port (which is hard to guess) could potentially connect if you haven’t added any authentication. So don’t share your pod URL publicly. For personal use, it’s fine.
• The model itself might output unintended or unsafe content. For example, it might describe people in an image with sensitive attributes or make incorrect statements. Use judgment, especially if using it in an application setting. Multimodal models can sometimes be prompted to divulge info about an image that might be private (like reading text on a screenshot with personal data, if it’s capable).
• If you upload images that you don’t have rights to or that contain sensitive info, treat the outputs carefully. LLaVA’s answers are not guaranteed to be 100% correct – don’t rely on it for critical analysis without verification.

With those considerations in mind, LLaVA on RunPod is a powerful tool. You now have your own vision-enabled AI ready to tackle image questions. This is like having a pair of AI “eyes” in the cloud that can interpret visuals and discuss them. It’s a testament to how far open-source AI has come, and platforms like RunPod make it accessible to everyone with a few clicks.

Finally, as always, if you found this interesting, why not spin up your own RunPod instance and try it out? The combination of visual and language AI has so many use cases – from educational tools, content creation, to accessibility (e.g., describing images to the visually impaired). With LLaVA 1.7.1 on RunPod, you are at the cutting edge of this multimodal revolution, all from the comfort of your browser. Happy building, and we can’t wait to see what you create with it! 🚀