ezlocalai is an easy set up artificial intelligence server that allows you to easily run multimodal artificial intelligence from your computer. It is designed to be as easy as possible to get started with running local models. It automatically handles downloading the model of your choice and configuring the server based on your CPU, RAM, and GPU specifications. It also includes OpenAI Style endpoints for easy integration with other applications using ezlocalai as an OpenAI API proxy with any model. Additional functionality is built in for voice cloning text to speech and a voice to text for easy voice communication as well as image generation and video generation entirely offline after the initial setup.

Prerequisites

• Python 3.10+
• Docker Desktop (Windows or Mac)
• CUDA Toolkit (May Need 12.4) (NVIDIA GPU only)
• ROCm (AMD GPU only - Linux)

Quick Start (Recommended)

Install the CLI and start ezlocalai with a single command:

pip install ezlocalai
ezlocalai start

It will take several minutes to download the models on the first run. Once running, access the API at http://localhost:8091.

CLI Commands

# Start with defaults (auto-detects GPU, uses Qwen3-VL-4B)
ezlocalai start

# Start with a specific model
ezlocalai start --model unsloth/gemma-3-4b-it-GGUF

# Start with custom options
ezlocalai start --model unsloth/Qwen3-VL-4B-Instruct-GGUF \
                --uri http://localhost:8091 \
                --api-key my-secret-key \
                --ngrok <your-ngrok-token>

# Other commands
ezlocalai stop      # Stop the container
ezlocalai restart   # Restart the container
ezlocalai status    # Check if running and show configuration
ezlocalai logs      # Show container logs (use -f to follow)
ezlocalai update    # Pull/rebuild latest images

# Send prompts directly from the CLI
ezlocalai prompt "Hello, world!"
ezlocalai prompt "What's in this image?" -image ./photo.jpg
ezlocalai prompt "Explain quantum computing" -m unsloth/Qwen3-VL-4B-Instruct-GGUF -temp 0.7

CLI Options

Prompt Command Options

For additional options (Whisper, image model, etc.), edit ~/.ezlocalai/.env:

Data Persistence

All data is stored in ~/.ezlocalai/:

Models persist across container updates - you won't re-download them when updating the CLI or rebuilding the CUDA image.

Benchmarks

Performance tested on Intel i9-12900KS + RTX 4090 (24GB):

Both models pre-calibrate at startup and hot-swap in ~1 second.

Distributed Fallback / Multi-Machine Setup

ezlocalai supports a distributed fallback system where multiple instances can fall back to each other when local resources (VRAM/RAM) are exhausted, or fall back to any OpenAI-compatible API. This enables:

• Load balancing: When one machine is busy, requests automatically route to another
• Redundancy: If one server is overloaded, the fallback handles requests
• Resource optimization: Each machine handles what it can, forwarding the rest
• Hybrid deployment: Mix local ezlocalai instances with cloud APIs

Configuration

Set these environment variables in your .env file or pass them to the container:

# Fallback server URL - can be another ezlocalai instance OR any OpenAI-compatible API
FALLBACK_SERVER=http://192.168.1.100:8091  # Another ezlocalai instance
# Or use a cloud provider:
# FALLBACK_SERVER=https://api.openai.com/v1

# Authentication for the fallback server
FALLBACK_API_KEY=your-api-key

# Optional: Override model for OpenAI-compatible fallback (pass-through by default)
# If not set, the originally requested model is passed through to the fallback server
# FALLBACK_MODEL=gpt-4o-mini

# Combined memory threshold (VRAM + RAM) in GB - fallback triggers when below this
# Models can offload to system RAM, so combined memory is more accurate than VRAM alone
FALLBACK_MEMORY_THRESHOLD=8.0

The system automatically detects whether FALLBACK_SERVER points to another ezlocalai instance or an OpenAI-compatible API by checking for the /v1/resources endpoint. If it's another ezlocalai server, full endpoint forwarding is used (preserving the original request). Otherwise, it falls back to standard OpenAI API calls, passing through the originally requested model (or using FALLBACK_MODEL if set as an override).

Example: Two-Machine Setup

Machine A (Primary with RTX 4090):

EZLOCALAI_URL=http://0.0.0.0:8091
EZLOCALAI_API_KEY=shared-key
FALLBACK_SERVER=http://machine-b:8091
FALLBACK_API_KEY=shared-key

Machine B (Fallback with RTX 3080):

EZLOCALAI_URL=http://0.0.0.0:8091
EZLOCALAI_API_KEY=shared-key
FALLBACK_SERVER=http://machine-a:8091
FALLBACK_API_KEY=shared-key

Both machines fall back to each other - creating a resilient two-node cluster.

Example: Local + Cloud Hybrid

Run a local ezlocalai with OpenAI as the fallback:

EZLOCALAI_URL=http://0.0.0.0:8091
FALLBACK_SERVER=https://api.openai.com/v1
FALLBACK_API_KEY=sk-your-openai-key
FALLBACK_MODEL=gpt-4o-mini

Monitoring Fallback Status

Check the fallback status via API:

# Get resource status including fallback info
curl http://localhost:8091/v1/resources

# Check fallback availability and models
curl http://localhost:8091/v1/fallback/status

What Gets Forwarded

When fallback is triggered to another ezlocalai instance, these endpoints are automatically forwarded:

• /v1/chat/completions - Chat completions (including streaming)
• /v1/completions - Text completions
• /v1/embeddings - Text embeddings
• /v1/audio/transcriptions - Speech-to-text
• /v1/audio/speech - Text-to-speech
• /v1/images/generations - Image generation
• /v1/images/edits - Image editing (image + text to image)

For OpenAI-compatible APIs, only chat completions and embeddings are forwarded.

Dedicated Voice Server

ezlocalai supports offloading TTS (text-to-speech) and STT (speech-to-text) processing to a dedicated voice server. This is useful when you want to:

• Separate workloads: Run voice models on a dedicated GPU while the main server handles LLMs
• Optimize resources: Keep voice models always loaded on a server with spare VRAM
• Reduce latency: Avoid lazy loading delays for voice requests

Configuration

Set the VOICE_SERVER environment variable:

# Option 1: Point to another ezlocalai server for voice processing
VOICE_SERVER=http://192.168.1.100:8091
VOICE_SERVER_API_KEY=your-api-key  # Optional, uses EZLOCALAI_API_KEY if not set

# Option 2: Make THIS server the voice server (keeps TTS/STT loaded)
VOICE_SERVER=true

Voice Server Mode (VOICE_SERVER=true)

When set to true, this server becomes a dedicated voice server:

• TTS (Chatterbox) and STT (Whisper) models are pre-loaded at startup and stay resident
• Voice models are never unloaded after requests (no lazy load/unload cycle)
• LLM models are still lazy-loaded as needed
• Ideal for a secondary server with a dedicated GPU for voice processing

Voice Passthrough Mode (VOICE_SERVER=<url>)

When set to a URL, voice requests are forwarded to that server:

• TTS and STT requests first try the voice server
• If the voice server fails or is unavailable, falls back to local processing
• LLM models run locally as usual
• No voice models are loaded locally unless the voice server is unavailable

Example: Two-Machine Voice Offload Setup

Machine A (Main LLM server with RTX 4090):

DEFAULT_MODEL=unsloth/Qwen3-Coder-30B-GGUF
VOICE_SERVER=http://machine-b:8091
VOICE_SERVER_API_KEY=shared-key

Machine B (Voice server with RTX 3090):

DEFAULT_MODEL=unsloth/Qwen3-4B-Instruct-GGUF  # Smaller LLM for basic tasks
VOICE_SERVER=true  # Keep voice models loaded

Machine A handles LLM inference while Machine B handles all voice processing with models always ready.

Wake Word Detection and Training

ezlocalai includes a complete wake word training and inference system that enables custom voice activation for AI assistants. When VOICE_SERVER=true, the wake word endpoints are enabled.

Custom Wake Word Architecture

ezlocalai trains wake word models using TTS-generated samples and exports them in multiple formats for cross-platform deployment:

graph LR
    A[User selects wake word] --> B[ezlocalai trains model]
    B --> C[Export: PyTorch .pt]
    B --> D[Export: ONNX .onnx]
    B --> E[Export: ESPDL .espdl]
    C --> F[Server inference]
    D --> G[Mobile App<br/>ONNX Runtime Mobile]
    E --> H[ESP32-S3<br/>ESP-DL Framework]

Supported Export Formats

The ESPDL format is quantized to int8 for efficient inference on microcontrollers, reducing the model size by ~75% while maintaining accuracy.

Model Architecture

• Input: 40 MFCC coefficients × 150 frames (1.5 seconds of audio at 16kHz)
• Architecture: Compact CNN with 3 conv layers + batch norm + max pooling
• Output: Single sigmoid probability [0.0, 1.0]
• Typical Accuracy: 95-98% validation accuracy

API Endpoints

# Train a new wake word model
curl -X POST "http://localhost:8091/v1/wakeword/train" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"word": "hey jarvis"}'

# Check training status
curl "http://localhost:8091/v1/wakeword/jobs/{job_id}" \
  -H "Authorization: Bearer your-api-key"

# List available models
curl "http://localhost:8091/v1/wakeword/models" \
  -H "Authorization: Bearer your-api-key"

# Download model for mobile (ONNX format)
curl "http://localhost:8091/v1/wakeword/models/hey%20jarvis?format=onnx" \
  -H "Authorization: Bearer your-api-key" \
  -o hey_jarvis_model.onnx

# Download model for ESP32-S3 (ESPDL format)
curl "http://localhost:8091/v1/wakeword/models/hey%20jarvis?format=espdl" \
  -H "Authorization: Bearer your-api-key" \
  -o hey_jarvis_model.espdl

# Download PyTorch model for server-side inference
curl "http://localhost:8091/v1/wakeword/models/hey%20jarvis?format=pytorch" \
  -H "Authorization: Bearer your-api-key" \
  -o hey_jarvis_model.pt

Client Integration

Mobile App (Flutter/Dart):

// Download model from ezlocalai
final response = await http.get(
  Uri.parse('$serverUrl/v1/wakeword/models/$wakeWord?format=onnx'),
  headers: {'Authorization': 'Bearer $apiKey'},
);
// Save and load with ONNX Runtime Mobile
final session = await OrtSession.create(modelPath);

ESP32-S3 (C with ESP-DL):

#include "custom_wakeword.h"

// Initialize the custom wake word system
custom_wakeword_init();

// Download model from server (stores in NVS)
custom_wakeword_download_model(
    "http://192.168.1.100:8091",  // ezlocalai server URL
    "your-api-key",               // API key (or NULL)
    "hey jarvis"                  // Wake word to download
);

// Start detection with callback
void on_wake_word(const char *word, float confidence, void *ctx) {
    printf("Wake word '%s' detected (%.2f confidence)\n", word, confidence);
}
custom_wakeword_start(on_wake_word, NULL);

Training Process

The wake word trainer:

1. Generates TTS samples using gTTS, Edge-TTS, and Chatterbox with various voices
2. Applies audio augmentation (noise injection, speed/pitch variations, reverb)
3. Trains a CNN model with MFCC features (40 coefficients)
4. Exports to all formats:
   • PyTorch (.pt) - full precision for server
   • ONNX (.onnx) - for mobile deployment
   • ESPDL (.espdl) - int8 quantized for ESP32

Training typically takes 3-5 minutes depending on your hardware.

OpenAI Style Endpoint Usage

OpenAI Style endpoints available at http://<YOUR LOCAL IP ADDRESS>:8091/v1/ by default. Documentation can be accessed at that http://localhost:8091 when the server is running.

import requests

response = requests.post(
    "http://localhost:8091/v1/chat/completions",
    headers={"Authorization": "Bearer your-api-key"},  # Change this if you configured an API key
    json={
        "model": "unsloth/Qwen3-VL-4B-Instruct-GGUF",
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Describe each stage of this image."},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://www.visualwatermark.com/images/add-text-to-photos/add-text-to-image-3.webp"
                        },
                    },
                ],
            },
        ],
        "max_tokens": 8192,
        "temperature": 0.7,
        "top_p": 0.8,
    },
)
print(response.json()["choices"][0]["message"]["content"])

For examples on how to use the server to communicate with the models, see the Examples Jupyter Notebook once the server is running. We also have an example to use in Google Colab.

Workflow

graph TD
   A[app.py] --> B[FASTAPI]
   B --> C[Pipes]
   C --> D[LLM]
   C --> E[STT]
   C --> F[CTTS]
   C --> G[IMG]
   D --> H[llama_cpp]
   D --> I[tiktoken]
   D --> J[torch]
   E --> K[faster_whisper]
   E --> L[pyaudio]
   E --> M[webrtcvad]
   E --> N[pydub]
   F --> O[TTS]
   F --> P[torchaudio]
   G --> Q[diffusers]
   Q --> J
   A --> R[Uvicorn]
   R --> S[ASGI Server]
   A --> T[API Endpoint: /v1/completions]
   T --> U[Pipes.get_response]
   U --> V{completion_type}
   V -->|completion| W[LLM.completion]
   V -->|chat| X[LLM.chat]
   X --> Y[LLM.generate]
   W --> Y
   Y --> Z[LLM.create_completion]
   Z --> AA[Return response]
   AA --> AB{stream}
   AB -->|True| AC[StreamingResponse]
   AB -->|False| AD[JSON response]
   U --> AE[Audio transcription]
   AE --> AF{audio_format}
   AF -->|Exists| AG[Transcribe audio]
   AG --> E
   AF -->|None| AH[Skip transcription]
   U --> AI[Audio generation]
   AI --> AJ{voice}
   AJ -->|Exists| AK[Generate audio]
   AK --> F
   AK --> AL{stream}
   AL -->|True| AM[StreamingResponse]
   AL -->|False| AN[JSON response with audio URL]
   AJ -->|None| AO[Skip audio generation]
   U --> AP[Image generation]
   AP --> AQ{IMG enabled}
   AQ -->|True| AR[Generate image]
   AR --> G
   AR --> AS[Append image URL to response]
   AQ -->|False| AT[Skip image generation]
   A --> AU[API Endpoint: /v1/chat/completions]
   AU --> U
   A --> AV[API Endpoint: /v1/embeddings]
   AV --> AW[LLM.embedding]
   AW --> AX[LLM.create_embedding]
   AX --> AY[Return embedding]
   A --> AZ[API Endpoint: /v1/audio/transcriptions]
   AZ --> BA[STT.transcribe_audio]
   BA --> BB[Return transcription]
   A --> BC[API Endpoint: /v1/audio/generation]
   BC --> BD[CTTS.generate]
   BD --> BE[Return audio URL or base64 audio]
   A --> BF[API Endpoint: /v1/models]
   BF --> BG[LLM.models]
   BG --> BH[Return available models]
   A --> BI[CORS Middleware]
   BJ[.env] --> BK[Environment Variables]
   BK --> A
   BL[setup.py] --> BM[ezlocalai package]
   BM --> BN[LLM]
   BM --> BO[STT]
   BM --> BP[CTTS]
   BM --> BQ[IMG]
   A --> BR[API Key Verification]
   BR --> BS[verify_api_key]
   A --> BT[Static Files]
   BT --> BU[API Endpoint: /outputs]
   A --> BV[Ngrok]
   BV --> BW[Public URL]