Last Updated: July 9, 2025
Deploy from the SaladCloud Portal.

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Overview

Inference is powered by Text Generation Inference (TGI), a toolkit for deploying and serving Large Language Models (LLMs) developed by Hugging Face. TGI provides optimized inference for text generation models with features like continuous batching, tensor parallelism, and efficient memory management. Several models are available, including:
  • Qwen 2.5 VL 3B Instruct - A 3 billion parameter vision-language model for multimodal understanding and generation tasks.
  • Qwen 2.5 VL 7B Instruct - A 7 billion parameter vision-language model with enhanced capabilities for complex multimodal tasks.
  • Qwen 3 8B - An 8 billion parameter language model with improved performance and efficiency.
  • Llama 3.1 8B Instruct - Meta’s 8 billion parameter instruction-tuned model for conversational AI and text generation.
  • Mistral 7B 0.3 Instruct - Mistral AI’s 7 billion parameter instruction-tuned model optimized for chat and reasoning tasks.
  • Llama 3.2 11B Vision-Instruct - Meta’s 11 billion parameter vision-language model for multimodal understanding and generation.
  • Custom Model - Deploy any TGI-compatible model by providing the model name via environment variable.

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Example request

Submit chat completion requests to the /v1/chat/completions endpoint, and receive generated text in response. 
curl -X POST https://vegetable-words-3e487ysdyhfkvjah.salad.cloud/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Salad-Api-Key: <YOUR_API_KEY>" \
-d @request.json
request.json
{
  "model": "tgi",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms:"
    }
  ],
  "max_tokens": 200,
  "temperature": 0.7,
  "top_p": 0.95,
  "frequency_penalty": 0.1,
  "stream": false
}
You will get back a json response with the generated text: 
{
  "id": "chatcmpl-1234567890abcdef",
  "object": "chat.completion",
  "created": 1704067200,
  "model": "tgi",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum computing is a revolutionary approach to computation that harnesses the principles of quantum mechanics..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 8,
    "completion_tokens": 156,
    "total_tokens": 164
  }
}

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How To Use This Recipe

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Authentication

When deploying this recipe, you can optionally enable authentication in the container gateway. If you enable authentication, all requests to your API will need to include your SaladCloud API key in the header Salad-Api-Key. See the documentation for more information about authentication.

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Replica Count

The recipe is configured for 3 replicas by default, and we recommend using at least 3 for testing, and at least 5 for production workloads. SaladCloud’s distributed GPU cloud is powered by idle gaming PCs around the world, in private residences, gaming cafes, and esports arenas. A consequence of this unique infrastructure is that all nodes must be considered interruptible without warning. If a 👨‍🍳 Chef (a compute host) decides they want to use their GPU to play a video game, or their dog trips on the power cord, or their Wi-Fi goes out, the instance of your workload running on that node will be interrupted, and a new instance will be allocated to a different node. This means you may want to slightly over-provision the capacity you expect to need in order to have adequate coverage during node reallocations. Don’t worry, we only charge for instances that are actually running.

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Logging

SaladCloud offers a simple built-in method to view logs from the portal, to facilitate testing and development. For production workloads, we highly recommend connecting an external logging source, such as Axiom. This can be done during container group creation.

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Deploy It And Wait

When you deploy the recipe, SaladCloud will find the desired number of qualified nodes, and begin the process of downloading the container image to the host machine. Depending on the preloaded model, the docker image can be quite large (Up to 22GB), and it may take up to tens of minutes to download to some machines, depending on the network conditions of that particular node. Remember, these are residential PCs with residential internet connections, and performance will vary across different nodes. Eventually, you will see instances enter the running state, and show a green checkmark in the “Ready” column, indicating the workload is passing its readiness probe. Once at least 1 instance is running, the container group will be considered running, but for production you will want to wait until an adequate number of nodes have become ready before moving traffic over. You will find helpful links and information in the readme on the container group page once deployed.

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Source Code

Github Repository