Lepton AI

Deploy large language models as production-ready HTTP endpoints without managing infrastructure. Lepton automatically allocates GPU resources based on model size and request volume, handling scaling, load balancing, and resource cleanup. Models are containerized and deployed across distributed GPU clusters with transparent resource management.

Automatically wraps deployed models with OpenAI API-compatible interfaces (chat completions, embeddings, image generation endpoints). Clients can use standard OpenAI SDKs and libraries without modification, with request/response schemas matching OpenAI's specification exactly. Supports streaming, function calling, and vision capabilities where applicable.

Tracks inference costs by model, user, and time period with granular billing based on actual resource consumption (GPU time, tokens generated, images processed). Provides cost forecasting and budget alerts. Supports cost attribution to different projects or departments. Integrates with accounting systems via API.

Streams model outputs token-by-token in real-time using HTTP Server-Sent Events (SSE) or WebSocket connections. Reduces perceived latency by showing first token within 100-500ms. Supports cancellation of in-flight requests. Includes token counting and cost estimation during streaming.

Deploy multiple LLMs, vision models, and custom models simultaneously on shared GPU infrastructure with request-time model selection. Routes requests to appropriate model based on task requirements, with built-in model versioning and A/B testing support. Models share GPU memory pools efficiently through dynamic allocation.

Automatically collects and visualizes inference metrics including latency, throughput, token counts, error rates, and GPU utilization without additional instrumentation. Provides dashboards showing per-model performance, cost tracking, and request tracing. Integrates with standard monitoring tools via Prometheus-compatible metrics endpoints.

Web-based interface for testing deployed models with real-time parameter adjustment (temperature, top-p, max-tokens, etc.) and response comparison. Supports batch testing with CSV inputs and exports results. Includes prompt engineering tools like variable substitution and few-shot example management. No code required.

Deploy custom inference logic written in Python (PyTorch, TensorFlow, ONNX, or custom code) as managed endpoints. Lepton handles containerization, GPU allocation, and scaling automatically. Supports model loading from local files, HuggingFace, or custom URLs. Includes dependency management and environment variable injection.

Deploy and serve image generation models (Stable Diffusion, DALL-E compatible) and vision models (image classification, object detection, visual QA) as APIs. Handles image encoding/decoding, batch processing, and GPU memory optimization for vision workloads. Supports both synchronous and asynchronous image generation.

Deploy embedding models (text, image, multimodal) that convert inputs to dense vector representations. Integrates with vector databases (Pinecone, Weaviate, Milvus) for semantic search and RAG applications. Supports batch embedding generation and automatic vector normalization. Handles tokenization and context window management.

Automatically batches multiple inference requests together to maximize GPU utilization and throughput. Supports asynchronous request submission with webhook callbacks or polling for results. Implements request queuing with configurable timeout and priority levels. Optimizes for latency-insensitive batch processing (e.g., embedding generation, image processing).

Deploy multiple versions of the same model simultaneously with traffic splitting for gradual rollouts. Supports A/B testing by routing a percentage of requests to new model versions. Includes automatic rollback on error rate thresholds. Maintains version history with easy rollback to previous versions.