sgl-project/sglang

Features

• OpenAI-Compatible APIs - Exposes a standard interface that allows existing applications to interact with hosted models as a drop-in replacement.
• Chat Completion Services - Exposes an API endpoint to receive user prompts and return model-generated text responses in a standard format.
• Large Language Models - Provides high-performance inference and serving for large language models with support for tensor parallelism.
• High-Throughput Model Serving - Deploys large language models via a standard API supporting high-throughput inference, streaming, and multi-modal inputs.
• Inference Engines - Provides a high-performance inference engine framework for serving large language models with complex workflow orchestration.
• Serving Frameworks - Serves as a production-ready inference engine for large language models with OpenAI-compatible API support.
• Disaggregated Inference - Separates compute-intensive prompt processing from memory-intensive token generation across distinct hardware nodes.
• Model Serving - Serves large language models via high-performance APIs supporting both request-response and streaming token generation.
• Distributed Inference Services - Distributes large-scale model workloads across multiple nodes and hardware devices for memory-intensive tasks.
• Generation Flow Orchestrators - Coordinates complex sequences of model calls, conditional logic, and parallel execution using a domain-specific language.
• Language Model Response Generators - Executes batch inference on large language models with synchronous or asynchronous streaming support.
• Continuous Batching Strategies - Maximizes hardware utilization by dynamically grouping incoming requests into batches during the inference cycle.
• Performance Optimizations - Maximizes hardware utilization using prefix caching, speculative decoding, and continuous batching.
• Model Serving APIs - Sends chat completion requests to a running model server using standard HTTP protocols to receive generated text responses.
• Prefill-Decode Disaggregation - Separates compute-intensive prefill and memory-intensive decoding phases across distinct hardware nodes to maximize throughput.
• Structured Output Enforcements - Enforces machine-readable output formats like JSON schemas during the inference process.
• Text Generation APIs - Produces text sequences from prompts using generative models with configurable sampling parameters.
• Dynamic Adapter Loaders - Loads and unloads low-rank adaptation modules at runtime via API calls without server restarts.
• Compute Graph Captures - Records and replays execution sequences to eliminate kernel launch overhead for predictable workloads.
• Embedding Generators - Converts text into vector representations using self-hosted language models via standard API endpoints.
• External Tool Integration - Processes function calls using standard schema definitions for external system interaction.
• Output Constraint Engines - Implements a specialized runtime that enforces grammar-based constraints and schemas on model responses during generation.
• Tool Calling - Invokes external functions by processing structured tool definitions during inference.
• Inference Acceleration - Provides high-throughput inference acceleration by utilizing speculative decoding and multi-token prediction to optimize the decoding phase.
• Inference Benchmarking Tools - Measures model throughput and latency by simulating concurrent request traffic with configurable parameters.
• Inference Scaling - Distributes large language models across multiple GPUs using tensor, data, and expert parallelism to handle larger model sizes.
• Response Generation Configurations - Streams text output incrementally as it is produced to reduce perceived latency.
• Context Window Management - Utilizes extended context windows to ingest and reason over large documents during inference requests.
• Chat and API Access - Provides standardized interfaces for chat and programmatic API access to served models.
• Model Inference and Serving - Coordinates request scheduling and tiered cache management across multiple inference instances to maximize throughput.
• Speculative Decoding Strategies - Provides configurable speculative decoding backends to accelerate token generation by verifying draft model predictions.
• Inference Configuration Parameters - Adjusts sampling behavior such as temperature and top-p to control the creativity and structure of model outputs.
• Offline Inference Engines - Provides high-throughput offline inference engines for batch processing large language models.
• Inference Optimizations - Provides high-performance inference optimizations including continuous batching, speculative decoding, and custom kernel execution to maximize throughput.
• Multi-Modal Input Processors - Incorporates image data into prompt sequences to enable vision-language model tasks.
• Vision-Language Models - Processes visual inputs using raw images or precomputed embeddings to generate text responses from multimodal models.
• Distributed Deployment Utilities - Distributes model computation across multiple hardware devices using tensor and data parallelism.
• Model Performance Optimization - Improves inference performance using expert parallelism, speculative decoding, and custom kernel tuning.
• Model Output Formatting - Enforces machine-readable output formats like JSON schemas and regular expressions during inference.
• Model Quantization - Reduces memory footprint and accelerates inference by configuring model precision and quantization parameters.
• Model Response Parsers - Extracts reasoning blocks and function calls from model responses using built-in parsers.
• Output Constraint Engines - Constrains model generation to a predefined set of choices using scoring methods to determine the most likely candidate.
• Model Serving Endpoints - Exposes standard completion endpoints for generating text responses from large language models.
• Model Serving Infrastructure - Supports scalable, distributed deployment of large language models across multiple hardware nodes using advanced parallelism strategies.
• Prefix Caching - Stores and shares common prompt prefixes across multiple requests to avoid redundant computation and improve time-to-first-token.
• Quantized Inference Runtimes - Executes quantized models using optimized runtimes to reduce memory footprint and improve inference throughput.
• Structured Output Parsers - Enforces machine-readable formats like JSON schemas and regular expressions during inference to ensure reliable structured output.
• Tensor Parallelism - Distributes model computation across multiple devices using tensor, pipeline, and data parallelism strategies to handle large-scale inference.
• Workflow Orchestration Primitives - Constructs complex multi-turn dialogues and prompt chains using standard control flow.
• Inference Optimization - Maximizes token generation rates using data-parallel attention and tensor parallelism.
• Reasoning Traces - Provides real-time visibility into internal model logic by streaming reasoning steps alongside final responses.
• Paged KV Cache Management - Implements paged key-value cache management to store and reuse intermediate attention states across requests.
• Inference Cache Management - Distributes key-value cache states across GPU, host RAM, and external storage to support extended context windows.
• Domain Specific Languages - Provides a programmable interface for orchestrating complex generation workflows and conditional logic.
• API Request Handling - Accepts inference tasks through standard endpoints to control sampling parameters and output length.
• Response Streaming Interfaces - Delivers generated tokens incrementally to provide immediate feedback.
• Agentic Workflow Orchestration - Coordinates complex sequences of model calls, tool invocations, and reasoning chains for autonomous agent applications.
• Distributed Inference Engines - Splits large language model layers across multiple physical nodes to improve throughput for long-context sequences.
• Distributed Model Orchestration - Manages multi-node model deployments on clusters to support large-scale models exceeding single-node memory capacity.
• Document Rerankers - Scores the relevance of a list of documents against a query using a cross-encoder model to improve retrieval accuracy.
• Structured Tool Invocations - Translates model-generated tool invocations into structured data formats for programmatic execution.
• Chunked Prefill Scheduling - Breaks large input processing tasks into smaller segments to allow for better interleaving with decode requests and reduce latency spikes.
• Hardware-Accelerated Inference - Executes large language and multimodal models on specialized hardware accelerators to improve throughput and latency.
• Inference Latency Optimizers - Reduces latency for low-concurrency workloads using draft model verification.
• Model Execution APIs - Invokes model generation through programmatic interfaces to build custom pipelines and reasoning chains.
• Multimodal Inference Engines - Integrates vision-language capabilities to process and analyze text, image, and video inputs within a unified inference pipeline.
• Inference Optimization - Eliminates kernel launch overhead by splitting large model computation graphs into smaller segments.
• Inference Acceleration Techniques - Increases generation throughput using multi-token prediction layers.
• High-Throughput Inference Services - Distributes model workloads across multiple devices using tensor, data, or expert parallelism to optimize performance.
• Hardware Acceleration - Utilizes specialized hardware acceleration and graph tuning to reduce latency and increase inference throughput.
• Model Loading - Configures model weight paths and tokenizer backends to initialize large language models.
• Unified Inference Pipelines - Integrates text, image, and video inputs into unified generation pipelines for vision-language model tasks.
• Mixture-of-Experts Inference Optimizers - Optimizes inference for mixture-of-experts models by distributing expert weights across multiple devices to overcome memory bottlenecks.
• Model Adapters - Supports dynamic loading and switching of low-rank adaptation modules during inference requests.
• Model Quantization - Reduces memory footprint by applying quantization methods like AWQ, FP8, and GPTQ during model loading.
• Weight Distribution - Splits model parameters across multiple devices to enable the execution of large models that exceed the memory capacity of a single hardware unit.
• Multimodal Document Processing - Extracts text and structure from images by sending visual data alongside text prompts to a compatible inference server.
• Multimodal Processing - Accepts image inputs within chat messages to perform vision-based analysis alongside text reasoning.
• Positional Embedding Techniques - Enables processing of ultra-long input sequences beyond native limits using positional embedding scaling techniques.
• Precision Quantization - Reduces the bit-width of weights and activations to decrease memory footprint and accelerate inference throughput.
• Reasoning Chains - Enables hybrid reasoning modes for structured step-by-step problem solving.
• Reasoning Configuration Tools - Separates internal reasoning steps from final output during streaming to allow visibility into the model's thought process.
• Reasoning Methodologies - Structures internal reasoning steps to provide insights into model decision-making during inference.
• Reasoning Mode Controllers - Supports toggling specialized reasoning modes to generate step-by-step thought processes.
• Reasoning Models - Extracts and separates internal thinking processes from final generated content to provide structured access to model reasoning.
• Reasoning Parsers - Provides structured access to internal thinking processes by separating reasoning steps from final content.
• Reasoning Token Budgeting - Controls reasoning output length and termination through configurable token budgets.
• Reasoning Trace Streaming - Delivers reasoning traces and final model outputs incrementally as they are generated.
• AI and Agents - A high-performance serving framework for large language models and multimodal models.
• Inference and Serving - Fast serving framework for language and vision models.
• Inference Engines - Fast serving framework for language and vision models.
• Inference Frameworks - Structured programming and serving framework for language models.
• KV Cache Management - Efficient programming and radix-based attention for LLMs.
• Model Serving - Fast serving framework for large language and vision models.
• Model Serving and Inference - High-performance serving framework for LLMs.
• Model Serving & Deployment - Provides a fast serving framework for LLMs and VLMs.
• Performance Caching Systems - Reduces GPU memory pressure by offloading cache data to host memory or external storage backends.
• Model Inference Clusters - Distributes large language model workloads across multiple physical machines or GPU clusters to increase throughput.
• Adapter Management - Enables dynamic loading and serving of multiple low-rank adaptation modules with optimized kernel backends.
• Model Parallelism Strategies - Implements tensor parallelism strategies to distribute large model weights across multiple processor cores.
• Metric and Performance Monitors - Collects and exports detailed performance statistics including latency and token throughput.
• Context Persistence - Maintains historical thinking traces within the conversation history to improve decision consistency in multi-turn agent scenarios.
• Reasoning Effort Budgets - Modifies the depth of model reasoning by adjusting thinking budgets to balance speed and complexity.
• Reasoning Process Monitors - Surfaces internal reasoning steps within API responses using unified configuration parameters.
• Custom Model Integrations - Adds support for new language models by implementing model-specific logic and registering them within the system for inference.
• External Tool Execution - Invokes external tools by parsing structured requests and returning tool-use commands during generation.
• Logit Processors - Adjusts token probabilities during generation to discourage repetition and control output diversity using logit processors.
• Parallel Prefill Strategies - Splits long input sequences across multiple compute ranks to reduce memory pressure and improve time-to-first-token.
• Diffusion Models - Provides capabilities for executing inference and serving tasks for diffusion-based generative models.
• Image Generation - Requests image generation from served models using text prompts and quality presets.
• Stage Disaggregation - Separates vision encoding, prefill, and decoding into independent tiers for optimized resource allocation.
• Native Tool Call Parsers - Supports structured function calling by parsing model output into standard formats and streaming incremental argument fragments.
• Context Partitioning - Partitions long input sequences across multiple devices to distribute memory storage and attention computation for extended context lengths.
• Performance Benchmarks - Simulates request traffic against various inference backends to measure throughput and latency for large language and vision models.
• Request Schedulers - Balances latency and throughput by managing request prioritization, preemption, and batching policies.
• Optimized Model Serving - Achieves high performance by executing large-scale models using specialized training bundles.
• Memory Optimization Techniques - Reduces GPU memory usage by offloading inactive cache data to host memory during decoding.
• Attention Backends - Accelerates attention mechanisms using specialized backends and parallelism strategies for multi-head latent attention.
• Attention Kernel Fusion - Combines multiple projection and attention operations into single optimized kernels to reduce memory bandwidth usage and improve processing speed.
• Dynamic Weight Updates - Refreshes inference engine weights dynamically between training steps to support various infrastructure configurations.
• Runtime State Controllers - Inspects model metadata, updates weights dynamically without restarting, and clears internal caches to maintain performance.
• Weight Offloading - Enables execution of large models by offloading components to CPU memory to free up device capacity.
• Multimodal Models - Extends model capabilities to process image inputs by defining custom processors, feature extractors, and vision-specific attention mechanisms.
• Reasoning Chain Parsers - Configures specialized parsers to process model-generated reasoning chains for structured thinking outputs.
• Reasoning Budget Controllers - Generates internal reasoning traces with configurable budgets to control the length and termination of the thinking process.
• Reasoning Depth Controllers - Provides runtime control over the depth and verbosity of model reasoning processes.
• Reasoning Process Controllers - Configures models to output structured thinking or reasoning processes alongside final answers.
• Sparse Attention Kernels - Improves inference efficiency by automatically activating native sparse attention mechanisms for supported model architectures.
• Text Tokenization Utilities - Converts text to token IDs and reconstructs text from IDs to facilitate external tokenization workflows.
• Distributed Caches - Connects to high-performance storage systems through a unified interface for scalable, cluster-wide cache management.
• Batch Processing Utilities - Executes prompt logic across multiple inputs simultaneously to improve throughput.
• Instance Replication - Replicates model instances across device groups to process multiple concurrent requests, optimizing memory and communication for high-demand workloads.
• Tool Selection Constraints - Constrains model behavior to specific tools or forces tool usage using grammar-based definitions.
• High-Performance Data Transfer - Minimizes latency during cache movement using zero-copy transfers and GPU-assisted I/O kernels.
• Response Streaming Utilities - Streams reasoning traces and final content incrementally to allow real-time visibility into the model's thought process.
• Dynamic Memory Allocation - Maximizes concurrency by adjusting the distribution of GPU memory between model weights and the cache pool.
• Token Probability Scorers - Computes log-probabilities or normalized scores for specific tokens to evaluate model outputs.
• Monitoring and Observability - Exposes comprehensive operational data through standard metrics, structured logging, and distributed tracing.
• Distributed Tracing - Merges profiling data collected across multiple nodes and parallelism types to identify bottlenecks in distributed model deployments.
• Performance Measurement - Evaluates model throughput and latency by simulating concurrent request traffic.
• AI Image Editing - Modifies input images based on natural language instructions using large language models.
• Model Orchestration and Management - Manages heterogeneous inference workers through a unified control plane for load monitoring and service discovery.
• Attention Mechanisms - Improves efficiency of attention architectures through specialized kernels and weight absorption techniques.
• Image Diffusion Models - Produces images from text prompts using large-scale diffusion models.
• Conversation State Management - Provides mechanisms for tracking conversation history and session context across multi-turn interactions.
• Expert Parallelism Configurations - Dynamically redistributes experts across devices to balance workloads and minimize idle time in sparse models.
• Chat Template Configurations - Allows overriding default tokenizer formats via built-in templates or custom definitions during server startup.
• Text-to-Image Generators - Processes text prompts to produce high-resolution images with configurable inference parameters.
• NPU Inference Execution - Executes large language model inference on specialized neural processing units using optimized backends for performance and hardware acceleration.
• Hardware Optimization Tools - Configures tensor parallelism and precision settings to optimize performance for specific hardware architectures.
• Hidden State Accessors - Enables retrieval of intermediate layer activations for downstream analysis and feature engineering.
• Just-In-Time Kernel Compilers - Exposes C++ functions to Python through a just-in-time compilation interface to support optimized kernel execution.
• TPU Inference Serving - Executes large language models on specialized cloud hardware using a backend optimized for high throughput and low latency.
• Model Inference Accelerators - Executes large language and diffusion models on specialized hardware to improve throughput and latency.
• Model Performance Benchmarking - Evaluates inference speed and output accuracy of deployed models across different hardware environments.
• Dynamic Optimizers - Provides dynamic adjustment of speculative decoding parameters to maintain inference efficiency during varying workloads.
• Asynchronous Computations - Optimizes execution by scheduling communication tasks concurrently with model computation to hide latency during complex operations.
• Adapter Execution Backends - Balances compatibility and high-concurrency performance for adapter-heavy workloads by selecting specialized backends.
• Mixture of Experts - Returns detailed routing information for mixture-of-experts models to support performance analysis.
• Expert Selection Analysis - Records and exports the selection frequency of experts in mixture-of-experts models to help optimize throughput and resource allocation.
• Hybrid - Leverages performance strengths by mixing and matching attention backends for prefill and decode phases.
• Hardware-Agnostic Deployment - Executes large language and diffusion models on diverse hardware architectures to maximize efficiency.
• NPU Accelerators - Executes large language and multimodal models on specialized neural processing units to optimize performance.
• Optical Character Recognition - Extracts text and structured table data from images by processing visual inputs through a multimodal language model interface.
• Online Quantization - Balances memory usage and computational efficiency by dynamically converting model weights during loading.
• Cross-Deployment Cache Sharing - Maximizes memory efficiency by enabling cross-cluster reuse of caches between different parallelism configurations.
• Reasoning State Persistence - Maintains reasoning content across multi-turn conversations to ensure context and logic persist.
• Sequence Parallelism Frameworks - Distributes long input sequences across multiple compute nodes to manage memory and compute requirements during inference.
• Token Prediction - Accelerates token generation throughput by combining speculative decoding with multi-token prediction.
• Token Bias Adjustments - Modifies token generation probabilities by applying bias values to internal scores during the inference process.
• Text-to-Speech - Synthesizes speech from text or audio input with low latency for conversational interactions.
• Vector Embeddings - Converts input text into vector representations using embedding models for downstream tasks.
• Video Generation - Creates video sequences from text prompts with configurable frame counts and inference steps.
• Video Clip Generators - Produces video content from text prompts and reference frames using dense or streaming pipelines.
• Voice Cloning - Replicates specific vocal characteristics from reference audio samples to generate personalized speech.
• Diffusion Acceleration Caches - Optimizes generation speed for diffusion models by caching intermediate computation blocks.
• Parallelism-Aware Cache Synchronization - Maintains high performance during distributed inference by transferring cache data between instances with different parallelism configurations.
• Storage Backend Adapters - Implements and registers external storage systems for key-value cache persistence through a standardized interface.
• Cache Quantization - Reduces memory usage by storing key-value pairs in lower-precision formats to support longer context lengths.
• Apple Silicon Inference - Executes large language models using optimized backends to improve performance on specific hardware with support for custom kernels and memory management.
• Containerized Service Deployments - Supports running model inference servers within isolated container environments for consistent deployment.
• Model Inference Deployment - Separates prefill and decoding stages onto different hardware resources for high-traffic deployments.
• Rate Limiting Policies - Protects system stability using circuit breakers, exponential backoff, and rate limiting policies.
• Request Routing - Distributes inference tasks across multiple servers using cache-aware logic to prevent bottlenecks.
• Runtime Configurations - Allows updating storage backend configurations at runtime without restarting the service.
• Traffic Load Balancers - Distributes inference traffic between prefill and decode engine instances to ensure load balancing at scale.
• Realtime Video Streamers - Generates a continuous stream of video frames from prompts and camera control signals.
• Engine State Persistence - Enables suspending and resuming inference engines by offloading weights and cache to free memory.
• RDMA Cache Streaming - Bypasses GPU bottlenecks by transferring prefill data directly into host memory across instances.
• Gateway Middleware - Executes custom request and response processing via middleware to implement organization-specific authentication, logging, or billing logic.
• Inference Task Interruption - Interrupts long-running inference tasks to allow for weight updates or batch reordering.
• Memory Layout Optimizations - Maximizes hardware efficiency by configuring static memory allocation and quantization paths.
• Health Monitoring Endpoints - Exposes diagnostic endpoints to verify service availability and operational status.
• Model Evaluation - Measures model performance by running automated benchmarks against an active inference server via a standard API.
• Performance Profiling - Collects operator-level execution data from hardware accelerators to identify bottlenecks.
• Tool Parsing Extensions - Integrates new model architectures by defining custom detection logic and tag configurations for parsing proprietary function call formats.

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