ezlocalai

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)

Additional Linux Prerequisites

• Docker
• Docker Compose
• NVIDIA Container Toolkit (NVIDIA GPU only)
• ROCm (AMD GPU only - Radeon RX 6000/7000/9000 series, Radeon PRO, and Ryzen APUs)

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/audio/music - Music generation
• /v1/images/generations - Image generation
• /v1/images/edits - Image editing (image + text to image)
• /v1/videos/generations - Video generation

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

On-Demand LLM Residency

DEFAULT_MODEL accepts a comma-separated model list. With LLM_MODEL_RESIDENCY=auto, ezlocalai estimates each configured model's GPU footprint at startup. Models remain resident together when they fit; when models assigned to the same GPU exceed its usable VRAM, only the first model is loaded at startup. Requesting another configured model unloads the idle resident model, loads the requested model, and leaves it resident until a different model is requested.

DEFAULT_MODEL=unsloth/Qwen3.8-27B-GGUF,unsloth/Qwen3.6-35B-A3B-MTP-GGUF
LLM_MODEL_RESIDENCY=auto
LLM_MODEL_RESIDENCY_MARGIN_GB=1.5

Use LLM_MODEL_RESIDENCY=resident to force all configured models to load together or LLM_MODEL_RESIDENCY=swap to force one-at-a-time loading. Swap mode serializes local text work so an active model is never unloaded mid-generation. The worker continues to advertise every configured model, but its heartbeat marks all swap-dependent model slots occupied while the resident model is in use.

Model-only load/unload timings are exposed under model_lifecycle in GET /v1/resources. To alternate configured models and print those timings:

python benchmark_model_lifecycle.py \
  --models unsloth/Qwen3.8-27B-GGUF,unsloth/Qwen3.6-35B-A3B-MTP-GGUF \
  --rounds 2

Qwen3.8-27B Performance Tuning

Qwen3.8-27B automatically uses MTP, through xllamacpp 2026.9.10809, with one inference slot, three draft tokens and a 0.1 draft probability threshold. DFlash2 remains opt-in with LLM_SPECULATIVE_TYPE=dflash2; only that backend downloads the revision-pinned Inco Q4_K_M draft (about 1.1 GB of weights, plus its runtime buffers) into the shared HF cache. The target model and sampling settings are unchanged: draft tokens are verified by the target, not accepted unconditionally. Physical prompt batches are hardware- and context-aware: 24 GB cards use an ubatch of 1024 through 200K context and 512 above 200K, while 32 GB cards use 1024. Other MTP model families retain their conservative defaults.

The CUDA image includes a pinned native hotfix for DFlash's large-image cache exhaustion (failed to process mtmd chunk). It preserves full-resolution target vision input without enlarging the KV cache. See hotfix details and native build instructions. Verify a deployed CUDA worker with:

docker exec ezlocalai python -c 'from xllamacpp._ezlocalai_hotfix import HOTFIX; print(HOTFIX)'

It should report dflash-pinned-image-positions-v1. The upstream version number alone does not identify the patched build. Test an idle worker's vision stream with python benchmark_vision.py --url http://localhost:8091 --stream (set EZLOCALAI_API_KEY if authentication is enabled).

All values remain operator-overridable:

LLM_SPECULATIVE_TYPE=auto  # auto, dflash2, mtp, none
MTP_SPEC_DRAFT_N_MAX=auto  # Qwen3.8-27B: 3 on all three card families
MTP_SPEC_DRAFT_P_MIN=auto  # Qwen3.8-27B: 0.1
KV_CACHE_TYPE=auto        # q4_0; q8_0 remains an explicit precision opt-in
DFLASH_SPEC_DRAFT_N_MAX=auto  # 3090: 3; 4090/5090: 4; explicit 1..7 overrides
DFLASH_SPEC_DRAFT_P_MIN=0.0
LLM_BATCH_SIZE=auto
LLM_UBATCH_SIZE=auto

GPU-aware defaults for Qwen3.8-27B (explicit settings take precedence):

These are starting profiles, not measured optima for every workload. To tune a mixed fleet from one configuration, use card-specific overrides such as MTP_SPEC_DRAFT_N_MAX_3090=3, MTP_SPEC_DRAFT_N_MAX_5090=4, or KV_CACHE_TYPE_5090=q8_0. Card-specific overrides beat global overrides. KV_CACHE_TYPE=auto selects the table; an existing explicit KV_CACHE_TYPE=q4_0 still keeps Q4 on every card unless overridden per card. Other model families and GPU types retain Q4 by default; Jetson keeps its explicit F16 setting. The memory planner uses the resolved cache precision. The local Q3 / 3090 Ti comparison found mixed DFlash gains, including regressions on short thinking requests. The MTP three-token baseline is shared across cards; higher values on the 4090/5090 require on-card validation, not extrapolation from free VRAM. Batch/ubatch remain hardware- and context-aware as described above; explicit operator values are preserved.

Deployment: remove an explicit LLM_SPECULATIVE_TYPE=dflash2 override or set it to auto/mtp, then rebuild/restart each worker. Explicit MTP_SPEC_DRAFT_* and KV_CACHE_TYPE* values still win; set them to auto (or remove them) to adopt these defaults. The router does not choose the native decoding backend. DFlash's optional starting lengths remain 3 on 3090 and 4 on 4090/5090.

For this 27B model, target KV at 262,144 tokens is approximately 4.5 GiB with Q4 versus 8.5 GiB with Q8 (excluding recurrent state, weights, draft and compute buffers). Q8 therefore needs about 4 GiB extra. It is a precision upgrade, not a guaranteed speed improvement. Keep Q4 on 24 GB cards at long context. A smaller main-model quant also does not guarantee faster prefill or decode: kernel choice, acceptance, cache traffic and prompt content matter.

An isolated sweep on an idle worker can be run with:

python benchmark_speculative.py --context 220000 --tokens 256 \
  --draft-lengths 3,4,5,7 --prompt-chars 0,120000,360000 --repeats 2 \
  --kv-cache q4_0

The sweep includes no-speculation and MTP controls, real code prefixes, actual prompt-token counts, prefill/decode timing, and greedy output hashes. Use the same immutable --prompt-file for all runs/cards. Allocated context alone is not a long-context benchmark: compare actual prompt lengths. Do not run this alongside the resident server on the same GPU. Greedy hash checks are a smoke test, not a proof of quality or of production-sampling throughput. Use --sampling-profile thinking or --sampling-profile instruct for the actual ezlocalai sampling settings; non-greedy runs do not compare output hashes. For a focused follow-up, --backends mtp,dflash2 skips the no-speculation control.

Single-GPU NVIDIA workers can also A/B test llama.cpp's experimental concurrent CUDA-stream optimization with GGML_CUDA_GRAPH_OPT=1. It primarily targets token-generation throughput rather than prompt processing. Results vary by GPU and model, so leave it disabled unless a representative decode benchmark shows a repeatable improvement.

Existing MTP_SPEC_DRAFT_* settings apply only with the MTP backend; they do not tune DFlash2. Set LLM_SPECULATIVE_TYPE=dflash2 for an A/B comparison or none to disable speculation. Other model families never receive the 27B draft. DFLASH_MODEL_FILE selects another quant from the same pinned repository; DFLASH_MODEL_PATH can point to an already downloaded compatible draft.

Benchmark representative prompts on each card: more speculation is not always faster, and a draft that forces target layers onto CPU can erase the benefit. Explicit LLM_UBATCH_SIZE values are attempted first; the resilient loader retries smaller physical batches if model initialization runs out of GPU memory.

Qwen TTS with llama.cpp

Local Qwen TTS now uses a persistent, private stdio worker built against the same llama.cpp revision as xllamacpp. It uses upstream libmtmd for the speaker encoder, talker, code predictor and vocoder. Standard images no longer install qwen-tts or its dedicated FlashAttention wheel. Torch/Transformers remain dependencies of other media models; they are not used for TTS synthesis.

The default retains the Qwen3-TTS-12Hz-0.6B-Base model, using the llama.cpp-compatible Q8_0 talker and F16 codec conversion. The old QWEN_TTS_MODEL=Qwen/Qwen3-TTS-12Hz-0.6B-Base value is migrated automatically. This is a backend/quantization change, not a claim of identical audio or verified perceptual-quality parity with the old implementation.

Voice .wav references, WAV responses, chunked PCM streaming, audio caching, and the existing voice/LLM slot-sharing policy are preserved. The native worker stays loaded between chunks and close() waits for process exit before the LLM can reclaim the slot. Cloning is audio-only (x-vector): .txt transcripts are preserved on disk but no longer condition generation. auto language uses script detection for Russian, Chinese, Japanese and Korean, otherwise English; specify the language explicitly for other supported languages (German, Italian, Portuguese, Spanish and French). Mixed-language synthesis should be checked on your actual voice samples.

QWEN_TTS_MODEL=Mouserat/qwen3-tts-0.6b-base-gguf
QWEN_TTS_CONTEXT_SIZE=4096
QWEN_TTS_THREADS=20
QWEN_TTS_TIMEOUT=300
QWEN_TTS_MAX_NEW_TOKENS=320  # audio frames, not text tokens

QWEN_TTS_MODEL_FILE and QWEN_TTS_MMPROJ_FILE accept filenames in a custom GGUF repository or local file paths; both files must be compatible with upstream libmtmd. QWEN_TTS_REVISION pins a custom repository revision. The old dtype, attention, transcript and non-streaming settings do not control the native model. QWEN_TTS_GENERATE_KWARGS supports max_new_tokens, temperature, top_k, top_p and repetition_penalty; unsupported options fail explicitly.

CUDA images build kernels for 3090/4090/5090 (SM 86/89/120). Native installs need CMake, a C++ compiler, and the CUDA toolkit for GPU synthesis:

python scripts/build_tts.py --cuda --build-dir /absolute/path/tts-build
export QWEN_TTS_BIN=/absolute/path/tts-build/bin/ezlocalai-tts

Omit --cuda for a CPU build. Model download happens during precache without loading a GPU model. Changing the backend uses a new audio-cache namespace.

Embeddings

ezlocalai serves /v1/embeddings with a dedicated GGUF embedding model, independent of TEXT_SERVER. By default it uses Qwen3-Embedding-0.6B Q8_0 with a 32k context:

EMBEDDING_ENABLED=true
EMBEDDING_MODEL=Qwen/Qwen3-Embedding-0.6B-GGUF
EMBEDDING_MODEL_ALIAS=Qwen3-Embedding-0.6B
EMBEDDING_QUANT_TYPE=Q8_0
EMBEDDING_CONTEXT_LENGTH=8192
EMBEDDING_N_PARALLEL=1
EMBEDDING_GPU_LAYERS=auto
EMBEDDING_KV_CACHE_TYPE=f16

When using the router, workers advertise the embedding capability only when EMBEDDING_ENABLED=true, so embedding requests route to workers that can serve them and the dashboard shows the active embedding model. EMBEDDING_N_PARALLEL controls how many full-context embedding model instances are loaded and reported to the router; each instance keeps the full EMBEDDING_CONTEXT_LENGTH per request instead of splitting the context across internal xllamacpp slots. With EMBEDDING_GPU_LAYERS=auto, ezlocalai estimates the 32k embedding cache footprint for each instance and partially offloads layers to CPU when VRAM is tight.

Image And Video

Image and video workers are opt-in so setting IMG_MODEL or VIDEO_MODEL alone does not warm-load or advertise those capabilities:

IMAGE_ENABLED=false
IMG_MODEL=
VIDEO_ENABLED=false
VIDEO_MODEL=unsloth/LTX-2.3-GGUF

Set IMAGE_ENABLED=true with IMG_MODEL to serve local image generation, or VIDEO_ENABLED=true to serve local video generation. When VIDEO_MODEL is omitted or blank, ezlocalai defaults to unsloth/LTX-2.3-GGUF. Enabled media models report image or video capacity to the router. They warm-load and stay resident when enough GPU headroom is available; on single-GPU LLM workers, image and video models can lazy-load after an LLM handoff so their pipelines initialize with freed VRAM. Workers with an IMAGE_SERVER URL configured still delegate media requests instead of loading local models.

For image generation, the default handoff policy is:

IMAGE_UNLOAD_LLM_DURING_GENERATION=auto
IMAGE_RELOAD_LLM_AFTER_GENERATION=true
IMAGE_WAIT_FOR_LLM_IDLE_TIMEOUT=60
IMAGE_MODEL_MIN_FREE_GB=6

In auto mode, a single-GPU worker keeps FLUX beside the LLM when there is enough free VRAM. Otherwise it waits for active LLM inference to finish, marks text and vision temporarily unavailable, unloads the LLM and idle auxiliary GPU models, loads FLUX, generates the image, unloads FLUX, and restores the exact LLM residency that existed before the handoff.

On a single-GPU worker, leave VIDEO_UNLOAD_LLM_DURING_GENERATION=auto. When a resident LLM is occupying the only GPU, ezlocalai marks the text/vision slots temporarily unavailable, unloads the LLM plus idle aux GPU models, initializes LTX-2.3 with the freed VRAM, runs generation, unloads video if needed, and reloads persistent LLMs when VIDEO_RELOAD_LLM_AFTER_GENERATION=true. VIDEO_GPU_RESIDENCY=auto chooses full GPU residency only on very large GPUs, model CPU offload when enough VRAM was freed for the requested clip size, and sequential CPU offload as the constrained fallback for long scenes. Short clips can use model offload when at least VIDEO_SHORT_MODEL_OFFLOAD_MIN_FREE_GB remains after LTX loads; longer scenes stay sequential unless VIDEO_MODEL_OFFLOAD_MIN_FREE_GB/VIDEO_FULL_GPU_MIN_FREE_GB say the GPU has room. If a more aggressive mode OOMs, VIDEO_RETRY_SEQUENTIAL_ON_OOM=true reloads LTX with sequential offload and retries once. Set VIDEO_GPU_RESIDENCY=full to force a full-GPU attempt.

LTX-2.3 requires dimensions divisible by 32 and frame counts in the 8n+1 pattern. The music-video helper handles frame planning automatically.

Music Generation

ezlocalai serves /v1/audio/music and /v1/audio/music/generations by starting an internal ACE-Step 1.5 acestep.cpp server inside the same Docker container. The GGUF model files are downloaded from Serveurperso/ACE-Step-1.5-GGUF into models/ace-step; acestep.cpp expects one LM GGUF, one Qwen3 embedding/text encoder GGUF, one DiT GGUF, and vae-BF16.gguf in that directory.

For the normal container-local setup, enable music on a worker with:

MUSIC_ENABLED=true

With no other music environment variables set, ezlocalai downloads and serves these ACE-Step 1.5 GGUF files:

MUSIC_MODEL=Serveurperso/ACE-Step-1.5-GGUF
ACE_STEP_SERVER_URL=
ACE_STEP_AUTO_START=true
ACE_STEP_MODELS_DIR=models/ace-step
ACE_STEP_LM_MODEL=acestep-5Hz-lm-4B-Q8_0.gguf
ACE_STEP_TEXT_ENCODER_MODEL=Qwen3-Embedding-0.6B-Q8_0.gguf
ACE_STEP_DIT_MODEL=acestep-v15-turbo-Q4_K_M.gguf
ACE_STEP_VAE_MODEL=vae-BF16.gguf
ACE_STEP_TIMEOUT=1800

The default LM is the 4B Q8 model for better music planning and lyric structure. Smaller nodes can override ACE_STEP_LM_MODEL to acestep-5Hz-lm-0.6B-Q8_0.gguf to save disk, RAM, and VRAM at the cost of quality.

Set ACE_STEP_SERVER_URL only if you intentionally run an external acestep.cpp process. When it is empty, ezlocalai starts /opt/acestep.cpp/build/ace-server on 127.0.0.1:8085 during startup.

On smaller single-GPU nodes, leave MUSIC_UNLOAD_LLM_DURING_GENERATION=auto. For container-local ACE-Step this marks the worker's text/vision slots unavailable, unloads resident LLMs before generation, runs the music job, then reloads persistent LLMs when MUSIC_RELOAD_LLM_AFTER_GENERATION=true.

Worker heartbeats also mark image, video, music, and combined music-video slots busy whenever those services require an LLM handoff and LLM inference is active. This prevents the router from dispatching work that cannot begin until the LLM becomes idle.

Example request:

curl http://localhost:8091/v1/audio/music \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Heavy metal anthem about the Pythagorean theorem, double-kick drums, distorted guitars, soaring vocals, and a triumphant chorus.",
    "lyrics": "[Verse]\nOn a right triangle battlefield\nTwo short sides raise their shields\n\n[Chorus]\nA squared plus B squared, lightning in the night\nEquals C squared, hypotenuse burning bright",
    "duration": 45,
    "keyscale": "E minor"
  }'

Music requests require prompt, lyrics, duration, and keyscale. The OpenAI-style model field is optional; if it is omitted or names an alias this worker does not advertise, ezlocalai uses the configured available music model (MUSIC_MODEL, default Serveurperso/ACE-Step-1.5-GGUF) instead of rejecting the request. Generation controls such as seed, bpm, timesignature, vocal_language, response_format, output_format, inference_steps, guidance_scale, shift, solver, lm_model, and synth_model are optional per-request overrides. Defaults are response_format=url, output_format=wav16, bpm=128, timesignature=4/4, vocal_language=en, inference_steps=16, guidance_scale=1.0, shift=3.0, and solver=euler.

For a live proof test against the internal or external ACE-Step server:

ACE_STEP_LIVE_TEST=true python -m unittest test_music_generation.LiveAceStepMusicProofTest

Music Video Generation

Workers that have both MUSIC_ENABLED=true and VIDEO_ENABLED=true advertise a combined music_video capability to the router. The router dashboard lists the combined music and video models next to that worker, and requests are routed to a worker that can perform both parts locally.

MUSIC_ENABLED=true
VIDEO_ENABLED=true

The music-video endpoint first generates the song with ACE-Step, then generates one or more LTX-2.3 video scenes in a single video session and muxes the generated song audio into the final MP4:

curl http://localhost:8091/v1/videos/music \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Heavy metal song about the Pythagorean theorem",
    "lyrics": "[Verse]\nA squared roars, B squared screams\n[Chorus]\nC squared lights the hypotenuse",
    "duration": 30,
    "keyscale": "E minor"
  }'

The required fields are prompt, lyrics, duration, and keyscale. Optional fields include model, music_model, video_model, video_prompt, scene_prompts, scene_duration, size, frame_rate, num_inference_steps, guidance_scale, video_guidance_scale, response_format, seed, music_seed, audio_url, include_scene_audio, storyboard, scene_images, bpm, timesignature, vocal_language, inference_steps, music_inference_steps, music_guidance_scale, shift, solver, lm_model, synth_model, audio_codes, image, and conditions. When audio_url points at an existing ezlocalai outputs/ audio file, the endpoint skips ACE-Step audio generation and only renders/muxes the video. Intermediate scene clips are video-only by default; set include_scene_audio=true only when debugging LTX's own generated audio. The final mux trims or pads the audio stream to the requested duration.

Music-video scenes use storyboard=true by default. When no explicit image is provided, ezlocalai draws deterministic first-frame storyboards from the prompt and lyrics, then uses LTX image-to-video so each scene has visible lyric/thematic anchors instead of drifting into generic concert footage. For maximum control, pass scene_images as base64/data-URL/HTTP images, one per planned scene. Pythagorean-theorem storyboards also burn in an exact a squared + b squared = c squared equation overlay using superscript-style exponents, since generated video models are unreliable at preserving formula text frame to frame.

LTX-2.3 audio-to-video is documented for roughly 20 seconds per request, with longer videos created by chaining clips. ezlocalai therefore defaults MUSIC_VIDEO_SCENE_DURATION=5 and caps each planned scene with MUSIC_VIDEO_MAX_SCENE_DURATION=20; the final response includes the generated song URL, each scene URL, the scene plan, and the final MP4 URL.

Router / Load Balancer Mode

For setups that outgrow point-to-point fallback (3+ machines, friends contributing GPUs, bittensor miners coming and going), ezlocalai can run as a dedicated router. The router itself loads no models — it accepts the normal OpenAI-compatible API and forwards each request to the best registered worker.

How it works

┌────────────┐  OpenAI API   ┌────────────────────┐   proxied request   ┌─────────────────┐
│  Client    │ ────────────▶ │  ezlocalai-router  │ ──────────────────▶ │  Worker (5090)  │
└────────────┘               │   (no models)      │                     ├─────────────────┤
                             │                    │ ◀── heartbeat ───── │  Worker (4090)  │
                             │  selects worker    │ ◀── heartbeat ───── │  Worker (voice) │
                             │  based on free     │ ◀── heartbeat ───── │  Worker (image) │
                             │  VRAM + queue +    │ ◀── heartbeat ───── │  Friend's 3090  │
                             │  capability +      │                     └─────────────────┘
                             │  model availability│
                             └────────────────────┘

Each worker is a normal ezlocalai instance with ROUTER_URL set. On startup the worker registers itself, then sends heartbeats every WORKER_HEARTBEAT_INTERVAL seconds containing free VRAM, queue depth, loaded models, and advertised capabilities (text, vision, tts, stt, embedding, image, video, music, music_video). Workers that miss ROUTER_WORKER_TTL seconds of heartbeats are pruned. If a request arrives and no suitable worker is free, the router keeps it queued until a worker becomes available when ROUTER_WAIT_TIMEOUT=0, or waits up to the configured positive timeout before returning 503.

The router creates a short dispatch lease only for text/vision requests so a stale heartbeat cannot immediately send a second LLM request to the same slot. The lease expires after ROUTER_RESERVATION_TTL seconds (default 15), after which worker heartbeat slot data is authoritative. TTS, STT, embedding, image, video, and music requests do not create router-side reservations.

Run the router

docker compose -f docker-compose-router.yml up -d

To enable managed text and vision overflow through Chutes, add the API key to the router's .env file. The model override is optional:

CHUTES_API_KEY=cpk_your-key
CHUTES_MODEL=Qwen/Qwen3.8-27B-TEE
# Multiple models are supported: CHUTES_MODEL=model-a,model-b

When the key is non-empty, the router adds a persistent Chutes.ai worker for /v1/chat/completions with text and vision capabilities at compute tier 45. Available internal workers at tier 45 or higher are always preferred; Chutes handles overflow after those workers are occupied. The virtual worker, its tier, request counts, and input/output token totals appear in the normal router dashboard and usage endpoints. Streaming Chutes requests automatically request the terminal OpenAI usage block so their token counts are captured. After each successful Chutes completion, the router refreshes the account's USD balance from Chutes in the background and caches it in the worker row until the next Chutes request. The cache is also seeded once when the router starts, so a configured account does not remain at Balance pending after deployment. A key without account-read permission leaves the balance display pending without affecting inference. Chutes is advertised with 100 concurrent slots. CHUTES_MODEL accepts a comma-separated list. Every configured model is advertised and requests retain the matching exact Chutes model ID; all models share the same 100-slot provider pool.

To add a lower-priority OpenRouter overflow pool, configure its key and optional model override:

OPENROUTER_API_KEY=sk-or-v1-your-key
OPENROUTER_MODEL=qwen/qwen3.8-27b
# Multiple models are supported: OPENROUTER_MODEL=model-a,model-b

The router adds OpenRouter.ai as a persistent tier-39 text/vision provider with 1,000 tracked concurrent slots. The normal order is internal t45-or-faster GPUs, Chutes t45, then OpenRouter t39. OpenRouter request/token usage appears in the same dashboard tables, and remaining credits are loaded at startup and refreshed after successful OpenRouter requests. Its hosted Qwen model is folded into the same Qwen3.8-27B dashboard group as the local GGUF and Chutes TEE variants.

OPENROUTER_MODEL also accepts a comma-separated list. Every configured model is advertised and dispatched using its matching OpenRouter ID while sharing the provider's 1,000 tracked slots. For both managed providers, chat requests preserve messages, multimodal content, streaming, token limits, temperature and sampling controls, stop/seed settings, tools, tool choice, and structured output fields. Qwen3.8 uses the same effective thinking/instruct profile as a local worker. The router translates ezlocalai's chat_template_kwargs and the standard reasoning/reasoning_effort controls into OpenRouter's unified reasoning object or Chutes/vLLM's chat-template options as appropriate.

Clients can opt out of managed fallback for an individual chat completion:

{
  "model": "unsloth/Qwen3.8-27B-GGUF",
  "messages": [{"role": "user", "content": "Hello"}],
  "disable_fallback": true
}

With disable_fallback=true, the request excludes Chutes and OpenRouter and waits without a router-side deadline until an internal worker is available. The flag is also honored by a directly addressed ezlocalai worker, preventing its configured FALLBACK_SERVER from being used for that request.

The router listens on port 8092 by default and exposes the same OpenAI-compatible endpoints as a normal ezlocalai server, plus:

• GET /dashboard — live HTML dashboard (auto-refresh, no auth)
• GET /v1/router/dashboard — same data as JSON (requires client key)
• GET /v1/router/workers — list registered workers and their live state
• GET /v1/router/health — router health + live worker count
• POST /v1/router/register / heartbeat / deregister — worker protocol

Point a worker at the router

For most setups, ROUTER_URL is the only env var you need to add to a worker. The worker self-reports its capabilities, GPUs (model names + VRAM), loaded models, and per-model context windows. The router uses the worker's existing EZLOCALAI_URL as the callback address, falling back to the connection source IP when that isn't reachable from the router.

Minimum config:

ROUTER_URL=http://router-host:8092

Optional overrides:

ROUTER_API_KEY=shared-key             # match the router's EZLOCALAI_API_KEY (or ROUTER_REGISTER_KEY)
WORKER_LABEL=main-5090                # friendly name (defaults to hostname)
WORKER_HEARTBEAT_INTERVAL=10          # seconds between heartbeats

Behind NAT or a tunnel? Set EZLOCALAI_URL to the public callback address (this is the same env var the worker already uses to advertise itself). Otherwise the router uses the source IP it sees on the registration, which is what you want for LAN workers.

Example: your current setup

Every worker just needs ROUTER_URL (and optionally WORKER_LABEL):

Clients then point to the router as if it were a single ezlocalai server:

curl https://router.you.com:8092/v1/chat/completions \
  -H "Authorization: Bearer shared-key" \
  -H "Content-Type: application/json" \
  -d '{"model":"unsloth/Qwen3.6-35B-A3B-GGUF","messages":[{"role":"user","content":"Hi"}]}'

The router picks the highest-scoring idle compatible worker at request time. If the fastest worker is already handling a request, the router spills over immediately to the best idle worker that can serve the request. Chutes is a t45 overflow pool after comparable or faster internal workers are occupied, and OpenRouter is the final managed pool at t39. If no compatible text/vision worker is free, it queues the request until a worker becomes available when ROUTER_WAIT_TIMEOUT=0, or waits up to the configured positive timeout before returning 503.

Reverse tunnel (workers without a public IP)

Some workers (CGNAT, friend's home machines, anything you can't port-forward) have no inbound network path the router can dial. Set WORKER_TUNNEL=true on the worker and that's it — the worker dials out over WebSocket to wss://<router>/v1/router/tunnel and the router multiplexes inference requests back through the same connection. No public IP, no port forward, no extra container.

# On the worker (only env vars required):
ROUTER_URL=https://router.you.com:8092
WORKER_TUNNEL=true
WORKER_LABEL=friend-3090            # optional
ROUTER_API_KEY=shared-key           # optional, must match router's EZLOCALAI_API_KEY

The worker registers with a sentinel URL tunnel://<worker_id>; you'll see it in /v1/router/workers and the dashboard like any other worker. Reconnects with exponential backoff (2s → 60s) are automatic. Streaming endpoints (chat SSE, audio) flow through unchanged. The only requirement is the worker can make outbound HTTPS to the router — same direction as the existing heartbeat.

Selection algorithm

Workers are scored each request as:

priority_tier = best_tier - 5 if tunneled else best_tier
score = priority_tier * 10  +  slots_left * 5  +  free_vram_gb  -  in_flight * 4

best_tier is derived from the worker's fastest GPU model (e.g. RTX 5090 ≈ 90, RTX 4090 = 80, RTX 3090 = 50, CPU = 2) and dominates the score, so an idle 5090 beats an idle 3090. Tunneled workers keep their reported best_tier but receive a 5-point priority-tier penalty so similarly capable direct workers are preferred. The load penalty (in_flight * 4) and idle-worker requirement keep burst traffic from stacking on a busy top-tier GPU while another compatible worker is free.

By default, text/vision routing requires an idle worker, so one long-running request on the 5090 sends the next compatible request to an idle 4090/3090 instead of stacking it onto the 5090. ROUTER_CROSS_MODEL_GRACE=0 means the router does not wait for a busy same-model worker before using the best available compatible fallback. ROUTER_IDLE_TIER_WINDOW=0 means the router does not hold back lower-tier idle workers while a higher-tier worker is busy; set a positive value to restrict idle spillover to workers within that many tier points of the fastest compatible tier. Set ROUTER_BUSY_SLOT_FALLBACK=true to restore slot-based routing when every compatible text/vision worker is already busy.

Requests that include the same prompt_cache_key keep affinity with the worker that owns their reusable prompt prefix. If that worker is briefly busy, the router waits up to ROUTER_PROMPT_AFFINITY_WAIT seconds (default 15) before using another worker. Temporary spillover does not replace the cache-owning worker, so later turns return to the warm prefix. The short default covers stream-release and heartbeat lag without queueing behind a long generation. Set the value to 0 to use immediate spillover.

For capability-only voice routing, the router defaults ROUTER_PREFER_DEDICATED_CAPABILITIES=stt, so large STT transcription jobs prefer workers that are not also serving text or vision. TTS routes by the normal score/tier calculation by default so low-latency playback can use faster mixed-capability workers. Stale ROUTER_PREFER_DEDICATED_CAPABILITIES=stt,tts values are treated as STT-only for TTS unless ROUTER_ALLOW_DEDICATED_TTS_PREFERENCE=true is also set. Large transcription jobs also use ROUTER_STT_TIMEOUT (default 7200 seconds) instead of the generic REQUEST_TIMEOUT.

Workers missing the required capability (text / vision / tts / stt / embedding / image / video / music) or the requested model are filtered out before scoring. Stale workers (no heartbeat for ROUTER_WORKER_TTL seconds) are also excluded.

You can inspect the live registry, including each worker's reported GPUs, tier, free VRAM, queue depth, and per-model context windows:

curl https://router.you.com:8092/v1/router/workers \
  -H "Authorization: Bearer shared-key"

Open pool vs. authenticated pool

Auth is intentionally simple: there is one shared secret for inference clients (EZLOCALAI_API_KEY) and one for workers (ROUTER_REGISTER_KEY, which falls back to EZLOCALAI_API_KEY if unset).

• Both empty → the router runs as an open pool: any client can submit requests, any worker can register. This is fine for a closed LAN; it is not safe to expose publicly. The router logs a [Router] OPEN POOL: ... warning at startup so you don't deploy this by accident.
• EZLOCALAI_API_KEY set, ROUTER_REGISTER_KEY unset → both clients and workers must present that one key.
• Both set, distinct values → clients use EZLOCALAI_API_KEY, workers use ROUTER_REGISTER_KEY. Use this when exposing the router publicly so you can rotate the worker secret independently of the client secret.

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

# Optional: load/report multiple local voice model instances
TTS_N_PARALLEL=1
STT_N_PARALLEL=1

Voice Server Mode (VOICE_SERVER=true)

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

• TTS (Qwen-TTS) 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

TTS_N_PARALLEL and STT_N_PARALLEL control how many separate local voice model instances are available on a dedicated voice worker. A mixed LLM/voice worker defaults to VOICE_UNLOAD_LLM_DURING_GENERATION=auto: TTS and STT do not stay warm beside the LLM. The router treats them as one shared worker slot, waits for active LLM work to finish, temporarily unloads the LLM, runs the voice request, then unloads the voice model and restores prior LLM availability. The LLM is reloaded eagerly only when configured below; otherwise the next text request loads it lazily. TTS and STT also exclude one another during this handoff. Set the option to false only when the worker has enough independent GPU capacity to keep voice models resident.

VOICE_WAIT_FOR_LLM_IDLE_TIMEOUT=60 controls the local race-safety timeout, and VOICE_RELOAD_LLM_AFTER_GENERATION=false controls eager restoration. It defaults to lazy restoration so native voice resources can fully unwind before the next text request reloads its LLM. Voice teardown explicitly closes the native model even while the request still owns its wrapper. An eager or subsequent restore first retries the exact context and GPU-layer residency used before the handoff, then uses the normal resilient fallback only if that known-good allocation no longer fits. Current context and GPU layers are visible in model_lifecycle.loaded_llm_runtime from GET /v1/resources. Service-specific TTS_... and STT_... forms of these variables override the shared values. In voice server mode, ezlocalai instead warm-loads the configured number of resident instances and reports that parallel capacity to the router.

Qwen-TTS voices live in the voices/ directory as .wav reference samples. The native backend uses audio-only speaker conditioning; matching .txt transcripts are not consumed.

Streaming Qwen-TTS now emits native PCM windows while synthesis continues, using upstream's unchanged 72-codec-frame vocoder window (about 5.76 seconds of audio, generated faster than playback on a suitable GPU). Short segments still finish before emitting audio. This is incremental audio output, not token-level text input: Qwen requires a complete text segment before starting it. The default chunking keeps the first chunk short for fast startup (QWEN_TTS_STREAM_FIRST_CHUNK_CHARS=120) and uses longer follow-up chunks (QWEN_TTS_STREAM_CHUNK_CHARS=280, capped by QWEN_TTS_MAX_CHUNK_CHARS) so playback has more audio buffered while Qwen generates the next block. PCM frames are flushed in QWEN_TTS_STREAM_WRITE_BYTES=16384 byte writes. Artificial drain delays and inter-chunk silence now default to zero; the legacy QWEN_TTS_STREAM_FRAME_DRAIN_SECONDS and QWEN_TTS_STREAM_FLUSH_SILENCE_MS overrides remain available.

Direct worker clients can use /v1/audio/speech/ws with the same Authorization header as HTTP. Send JSON {"text":"Hello there.","flush":true} followed by more text messages while audio is being generated; finish with {"done":true}. Without explicit flush, complete sentences are submitted after 50 characters (or a word boundary after 350). Input is bounded to four pending segments and a 4096-character buffer, with a 30-second input/output inactivity timeout. Idle sessions do not reserve the LLM slot. Binary responses form one stream: an 8-byte little-endian sample-rate/bit-depth/channel header (<IHH), then 4-byte little-endian PCM byte lengths and their PCM16 bodies, then one zero length and a legacy empty WebSocket message. Message boundaries need not match audio frame boundaries. Disconnects cancel native generation before releasing the voice lease.

The existing HTTP streaming endpoint remains wire-compatible, so WorkConductor's PCM consumer benefits without changes. Its sentence-level text scheduling is still required; neither WorkConductor nor the router is switched to this direct worker WebSocket protocol by this change.

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 and Edge-TTS 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]