VLM Inference Server

A high-performance, production-ready Vision-Language Model (VLM) inference server built entirely in Rust

Transform images and text into insights with this OpenAI-compatible inference server powered by real AI models (LLaVA 1.5) and the Candle ML framework.

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Why This Project Exists

Modern AI applications need to understand both images and text together - whether it's analyzing medical scans, describing product images, or answering questions about visual data. But deploying vision-language models (VLMs) is challenging:

• Complex Infrastructure: Most solutions require Python, CUDA, multiple dependencies
• Poor Performance: High latency, memory inefficiency, difficult to scale
• Vendor Lock-in: Cloud-only solutions with high costs

We built this to solve those problems with a pure Rust implementation that's:

• Fast: Low-latency inference with Metal GPU support (Apple Silicon) and CPU fallback
• Efficient: 14GB model running on consumer hardware
• Production-Ready: OpenAI-compatible API, streaming support, proper error handling
• Easy to Deploy: Single binary, no Python required

📖 Read the full story in our deep-dive blog post →

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Quick Start

Get up and running in 5 minutes:

Prerequisites

• Rust 1.70+: Install Rust
• 8GB+ RAM: 16GB recommended for optimal performance
• macOS or Linux: Apple Silicon (M1/M2/M3) or x86_64

Installation

# Clone the repository
git clone https://github.com/mixpeek/multimodal-inference-server.git
cd vlm-inference-server

# Build the project (Release mode for best performance)
cargo build --release

# Start the worker (downloads 14GB model on first run)
./target/release/vlm-worker --host 0.0.0.0 --port 50051 &

# Start the gateway (HTTP API)
./target/release/vlm-gateway --host 0.0.0.0 --port 8080 &

Your First Request

# Send a chat completion request
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-prod",
    "messages": [{"role": "user", "content": "Hello, how are you?"}],
    "max_tokens": 20
  }'

That's it! You now have a VLM inference server running locally.

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Features

🚀 Production-Ready

• OpenAI-Compatible API: Drop-in replacement for OpenAI's chat completions endpoint
• Streaming Support: Real-time token streaming via Server-Sent Events (SSE)
• Health Checks: /healthz and /readyz endpoints for Kubernetes
• Graceful Shutdown: Proper cleanup and connection draining

🧠 Real AI Models

• LLaVA 1.5 7B: State-of-the-art vision-language model
• CLIP Vision Encoder: High-quality image understanding
• LLaMA-2 Text Generation: Powerful language model
• Automatic Downloads: Models fetched from HuggingFace Hub

⚡ High Performance

• Pure Rust: Memory-safe, zero-cost abstractions
• Candle ML Framework: Fast tensor operations, Metal GPU support
• gRPC Communication: Efficient gateway ↔ worker communication
• KV Cache: Optimized attention caching for generation

🛠️ Developer Friendly

• Modular Architecture: Clean separation of concerns
• Trait-Based Design: Easy to swap ML backends
• Comprehensive Tests: Unit, integration, and GPU tests
• Rich Documentation: API docs, architecture guides, examples

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Architecture

┌─────────────┐          ┌──────────┐          ┌─────────────┐
│   Client    │          │ Gateway  │          │   Worker    │
│  (HTTP)     │─────────▶│ (HTTP)   │─────────▶│  (gRPC)     │
│             │◀─────────│          │◀─────────│             │
└─────────────┘          └──────────┘          └─────────────┘
                               │                       │
                               │                       ▼
                               │              ┌─────────────────┐
                               │              │  Candle Engine  │
                               │              │  ┌───────────┐  │
                               │              │  │   CLIP    │  │
                               │              │  │  Vision   │  │
                               │              │  └───────────┘  │
                               │              │  ┌───────────┐  │
                               │              │  │  LLaMA-2  │  │
                               │              │  │    LLM    │  │
                               │              │  └───────────┘  │
                               │              └─────────────────┘
                               │
                               ▼
                      ┌──────────────────┐
                      │   Observability  │
                      │  Metrics, Logs   │
                      └──────────────────┘

Learn more: Architecture Documentation →

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Usage

Basic Text Completion

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-prod",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "max_tokens": 100,
    "temperature": 0.7
  }'

Streaming Response

curl -N -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-prod",
    "messages": [{"role": "user", "content": "Write a haiku about Rust"}],
    "max_tokens": 50,
    "stream": true
  }'

Vision + Text (Multimodal)

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-prod",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "What'\''s in this image?"},
        {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQ..."}}
      ]
    }],
    "max_tokens": 100
  }'

More examples: examples/

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API Reference

Endpoints

Endpoint	Method	Description
/v1/chat/completions	POST	Create a chat completion (OpenAI-compatible)
/healthz	GET	Health check
/readyz	GET	Readiness check
/v1/models	GET	List available models

Request Parameters

Parameter	Type	Required	Default	Description
model	string	Yes	-	Model identifier (e.g., "vlm-prod")
messages	array	Yes	-	Conversation messages
max_tokens	integer	No	256	Maximum tokens to generate
temperature	float	No	1.0	Sampling temperature (0.0-2.0)
top_p	float	No	1.0	Nucleus sampling parameter
stream	boolean	No	false	Enable streaming responses
stop	string/array	No	-	Stop sequences

Full API documentation: docs/API.md

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Performance

Benchmarks on Apple M3 Ultra (192GB RAM, CPU mode):

Metric	Value
Model Loading	~30s (one-time)
Vision Encoding	100-200ms per image
Prefill (256 tokens)	500ms-1s
Decode	100-200ms per token
End-to-End (20 tokens)	2-5s
Memory Usage	~16GB

Performance tuning: docs/PERFORMANCE.md

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Development

Project Structure

vlm-inference-server/
├── crates/
│   ├── api_types/          # OpenAI-compatible API types
│   ├── proto/              # gRPC protocol definitions
│   ├── gateway/            # HTTP edge service
│   ├── worker/             # Inference worker service
│   ├── engine/             # ML engine trait definitions
│   ├── engine_adapters/
│   │   ├── mock_engine/    # Test mock
│   │   └── candle_engine/  # Candle ML implementation
│   ├── multimodal/         # Image preprocessing
│   ├── scheduler/          # Batching & admission control
│   ├── kv_cache/           # Key-value cache management
│   ├── sampling/           # Token sampling strategies
│   ├── common/             # Shared utilities
│   └── observability/      # Tracing & metrics
├── docs/                   # Documentation
├── examples/               # Usage examples
└── scripts/                # Helper scripts

Running Tests

# Run all tests
cargo test --workspace

# Run specific crate tests
cargo test --package vlm-candle-engine

# Run with logging
RUST_LOG=debug cargo test

# Run integration tests
cargo test --test '*'

Building from Source

# Debug build (faster compile, slower runtime)
cargo build

# Release build (optimized)
cargo build --release

# With specific features
cargo build --bin vlm-worker --features candle --release

# Check code style
cargo fmt --all -- --check
cargo clippy --all-targets --all-features

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Deployment

Docker

# Build image
docker build -t vlm-inference-server .

# Run container
docker run -p 8080:8080 -p 50051:50051 vlm-inference-server

Kubernetes

# See k8s/ directory for full manifests
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

Systemd

# See systemd/ directory for service files
sudo cp systemd/vlm-worker.service /etc/systemd/system/
sudo cp systemd/vlm-gateway.service /etc/systemd/system/
sudo systemctl enable --now vlm-worker vlm-gateway

Deployment guides: docs/DEPLOYMENT.md

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Contributing

We love contributions! Whether you're:

• 🐛 Reporting a bug
• 💡 Suggesting a feature
• 📝 Improving documentation
• 🔧 Submitting a pull request

Please read our Contributing Guide first.

Development Setup

1. Fork and clone the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Run tests: cargo test --workspace
5. Commit: git commit -m "Add amazing feature"
6. Push: git push origin feature/amazing-feature
7. Open a Pull Request

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FAQ

Q: Can this run on GPU?

A: Yes! The Candle engine supports Metal (Apple Silicon), CUDA (NVIDIA), and CPU. Metal support is enabled by default on macOS.

Q: What models are supported?

A: Currently LLaVA 1.5 7B. The modular architecture makes it easy to add other models (see Adding Models).

Q: Is this production-ready?

A: Yes! It handles real inference with proper error handling, health checks, and observability. The main limitation is tokenization (tokens shown as tok{id} instead of decoded text).

Q: How much memory do I need?

A: Minimum 8GB, recommended 16GB+. The 14GB model is memory-mapped, so it doesn't all load into RAM at once.

Q: Can I use this commercially?

A: Yes! This project is licensed under Apache 2.0, which permits commercial use.

More questions? Check the full FAQ or open an issue.

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Roadmap

• Phase 0: Core infrastructure (gateway, worker, protocols)
• Phase 1: Candle VLM engine integration
• Phase 2: Real model inference (LLaVA 1.5 7B)
• Phase 3: Production hardening
  • Real tokenizer integration
  • Image preprocessing pipeline
  • Paged KV cache (vLLM-style)
  • Flash Attention support
• Phase 4: Advanced features
  • Multi-model support
  • Dynamic batching
  • Model quantization (int8/int4)
  • Distributed inference

See the full roadmap: docs/ROADMAP.md

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Acknowledgments

This project builds on incredible work from the ML and Rust communities:

• Candle: Minimalist ML framework from HuggingFace
• LLaVA: Visual instruction tuning research
• Tonic: gRPC framework for Rust
• Axum: Web framework from the Tokio team

Special thanks to:

• HuggingFace for open-sourcing Candle and hosting models
• The Rust community for amazing tools and libraries
• All contributors who helped make this project better

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License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Copyright 2026 VLM Inference Server Contributors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Support

• Documentation: docs/
• Examples: examples/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Blog Post: Why and How We Built This →

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Star History

If you find this project useful, please consider giving it a star ⭐️

It helps others discover the project and motivates continued development!

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Built with ❤️ using Rust

Quick Start • Features • Architecture • Contributing • Blog Post

Sources

This README was crafted following best practices from:

• GitHub - README Best Practices
• Tilburg Science Hub - README Best Practices
• FreeCodeCamp - How to Write a Good README
• GitHub Docs - Repository Best Practices