InstantGPU Guides

Follow practical tutorials for building and deploying AI workloads on InstantGPU.

Whether you are serving open-source large language models, running high-throughput image generation pipelines, setting up vector search with RAG, or hardening containerized deployments for production traffic, our comprehensive walkthroughs guide you from zero to production.

Deploy an LLM #

Learn how to deploy a large language model and expose it for inference with an OpenAI-compatible API.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.instantgpu.ai/v1",
    api_key="YOUR_INSTANTGPU_API_KEY"
)

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are an AI assistant deployed on InstantGPU."},
        {"role": "user", "content": "Explain how PagedAttention speeds up LLM inference."}
    ],
    temperature=0.7,
    max_tokens=512
)

print(response.choices[0].message.content)

Deploy Image Generation #

Set up an image-generation workload and connect it to an application. Deploy state-of-the-art models like Flux.1 Schnell/Dev or Stable Diffusion XL with TensorRT acceleration to achieve sub-second image synthesis with REST endpoint triggers.

Deploy Video Generation #

Configure GPU infrastructure for AI video-generation workloads. Run memory-intensive diffusion video pipelines (CogVideoX, Wan2.1, SVD, and AnimateDiff) requiring high VRAM GPUs like the NVIDIA A100 (80GB) or H100 with offloading and temporal attention optimizations.

Build an Embeddings & RAG Application #

Generate embeddings and connect them to a retrieval workflow. Learn how to deploy Text Embeddings Inference (TEI) with high throughput, ingest markdown and PDF documents, store dense vectors in vector databases, and execute semantic retrieval augmented generation.

Deploy vLLM #

Deploy an LLM using vLLM for efficient model serving. vLLM is the gold standard for high-throughput serving with PagedAttention, chunked prefill, and dynamic continuous batching.

docker run --gpus all -d -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model meta-llama/Meta-Llama-3-8B-Instruct \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.92

Deploy ComfyUI #

Launch a GPU-powered ComfyUI environment for image-generation workflows. Connect to the graphical node interface in your browser, install custom nodes via ComfyUI-Manager, load custom checkpoint models, and trigger workflow graphs programmatically via WebSocket and REST APIs.

Deploy a Custom Docker Image #

Bring your own container and run a custom AI environment on InstantGPU. Configure registry credentials, set resource limits, specify port mappings, and automate updates via webhooks whenever new container tags are pushed.

Production Deployment #

Learn how to prepare your AI workload for production traffic, monitoring, scaling, and reliability: