Multi-Model GPU Deployment
Deploy multiple AI models on a single GPU card without resource conflicts. Float16 GPU Platform automatically manages model loading and request queuing, preventing GPU collapse and eliminating the need for complex model serving configuration.
Start deploying multiple models without GPU management hassle!
How It Works
Why Choose Multi-Model Deployment?
Efficient Resource Usage
GPU resources are only occupied when processing requests. No idle GPU consumption.
Auto Request Queuing
Automatic queuing system prevents GPU collapse during concurrent requests.
Zero Configuration
No need to configure complex model serving software. Deploy and go.
Multiple Model Types
Support for LLM, VLM, and Embedding models from 4B to 32B parameters.
Common Use Cases
Discover how multi-model deployment can accelerate your AI development workflow
Multi-Modal AI Applications
Deploy both text and vision models together for comprehensive AI-powered applications.
Development & Testing
Test multiple model versions simultaneously without managing separate GPU instances.
Startup MVP Development
Launch multiple AI features quickly without infrastructure complexity or high costs.
Research Experiments
Run experiments across different models and sizes without GPU management overhead.
Perfect For
Supported Model Types
Large Language Models (LLM)
- Qwen3 (4B - 32B)
- Gemma Models
- Custom Fine-tuned Models
Vision Language Models (VLM)
- Qwen2.5 Vision
- UI-TARS Vision Models
Embedding Models
- BGE-M3 Multilingual
- Qwen3 Embeddings
Key Features
Zero GPU occupation until request arrives
Automatic request queuing for concurrent calls
No model serving software configuration needed
Support LLM, VLM, and Embedding models
Multiple model sizes (4B to 32B parameters)
Function calling & JSON output support
Vision and text processing
Multilingual support
Technical Specifications
Quick Start Example
# Deploy multiple models with Float16
from openai import OpenAI
client = OpenAI(
api_key="your-float16-api-key",
base_url="https://api.float16.cloud/v1"
)
# Use different models without configuration
response = client.chat.completions.create(
model="qwen3-14b",
messages=[{"role": "user", "content": "Hello!"}]
)
# Switch models instantly
vision_response = client.chat.completions.create(
model="qwen2.5-vision",
messages=[{"role": "user", "content": "Analyze this image"}]
)
# GPU resources managed automatically
# No manual configuration needed!