SGLang

Implements a radix tree-based prefix cache that maps input token sequences to pre-computed KV cache blocks, enabling reuse of attention computations across requests with shared prefixes. The system maintains a token-to-KV mapping layer that tracks which tokens map to which cached KV states, allowing the scheduler to skip redundant computation during the prefill phase when requests share common prompt prefixes. This is integrated directly into the memory management and KV cache allocation system.

Encodes output constraints (JSON schemas, regex patterns, grammar rules) into a compressed finite state machine that guides token sampling at generation time. The system compiles constraints into state transitions that restrict which tokens are valid at each step, enforcing structural validity without post-hoc filtering or rejection sampling. This is integrated into the logits processing pipeline, allowing the sampler to skip invalid tokens before probability computation.

Enables loading and switching between LoRA (Low-Rank Adaptation) adapters at runtime without reloading the base model. The system maintains a LoRA registry, loads adapter weights into GPU memory, and integrates adapter application into the model forward pass through a linear layer wrapper. This allows serving multiple fine-tuned variants of the same base model with minimal memory overhead (typically 1-5% per adapter).

Implements a sophisticated scheduler that forms batches of requests, manages prefill (prompt processing) and decode (token generation) phases separately, and optimizes batch composition for GPU utilization. The system tracks request state (waiting, prefilling, decoding, finished), dynamically adds/removes requests from batches, and can disaggregate prefill and decode into separate GPU kernels to maximize parallelism. This enables serving many concurrent requests with high GPU utilization.

Implements a multi-process server architecture where a main process manages request routing and scheduling, while worker processes handle model execution. The system uses inter-process communication (IPC) to pass requests and responses between processes, and maintains a centralized TokenizerManager that handles tokenization/detokenization for all workers. This enables better resource isolation, fault tolerance, and scalability across multiple GPUs or CPU cores.

Implements tensor parallelism by partitioning model weights across multiple GPUs and using all-reduce collective communication to synchronize gradients/activations. The system uses NCCL (NVIDIA Collective Communications Library) for efficient GPU-to-GPU communication, and integrates tensor parallelism into the linear layer execution through a distributed communication wrapper. This enables serving models larger than single-GPU memory by splitting computation across devices.

Implements expert parallelism for Mixture-of-Experts (MoE) models by distributing expert computation across GPUs and routing tokens to appropriate experts based on learned routing weights. The system maintains a token-to-expert mapping that determines which tokens go to which experts, handles load balancing to prevent expert overload, and integrates expert dispatch into the model execution pipeline. This enables efficient serving of MoE models like DeepSeek and Mixtral by parallelizing expert computation.

Provides a Python API for direct programmatic access to the SGLang inference engine, allowing applications to call the model without HTTP or gRPC overhead. The API exposes core functions like `generate()` and `chat()` that accept prompts and return generated text, with full control over generation parameters and access to internal state. This enables embedding SGLang directly in Python applications without network communication.

Automatically selects and configures parallelism strategies (tensor parallelism across GPUs, pipeline parallelism across layers, expert parallelism for MoE models) based on model size, GPU count, and hardware topology. The system analyzes the model architecture and available resources, then partitions computation across devices using distributed communication primitives (all-reduce, all-gather, reduce-scatter). This is implemented through a ModelConfig system that determines optimal parallelism configuration and a multi-platform layer abstraction that handles device-specific communication.

Compiles the model forward pass (prefill and decode phases) into CUDA graphs that capture GPU kernel launches and memory operations, then replays these graphs for each batch without CPU-GPU synchronization overhead. The system maintains separate graphs for different batch sizes and sequence lengths, dynamically selecting the appropriate graph at runtime based on current batch composition. This eliminates CPU-GPU round-trip latency and reduces kernel launch overhead by 10-100x compared to eager execution.

Implements a hierarchical KV cache storage system (HiCache) that can spill cache data to CPU RAM, NVMe SSD, or cloud object storage when GPU VRAM is exhausted, with automatic prefetching and transfer optimization. The system maintains a storage backend abstraction layer that supports multiple backends (GPU VRAM, CPU pinned memory, NVMe, S3) and intelligently moves data between tiers based on access patterns and available bandwidth. This enables serving longer sequences or larger batch sizes than GPU memory alone would allow.

Implements speculative decoding using a lightweight EAGLE draft model that generates multiple candidate tokens in parallel, which are then verified against the main model in a single forward pass. The system integrates the draft and verification flow into the scheduler, allowing draft tokens to be speculatively executed while the main model processes other requests, reducing latency for long generations. The draft model is trained to predict the main model's outputs with high accuracy while being 5-10x faster.

Exposes an HTTP server that implements the OpenAI Chat Completions API specification, automatically handling chat template formatting, conversation history management, and response serialization. The system maintains a ChatTemplate registry that maps model names to their specific prompt formatting rules (e.g., Llama, Mistral, Qwen), automatically applying the correct template to convert user messages into model-compatible prompts. This enables drop-in replacement of OpenAI API calls with local SGLang deployments.

Exposes a gRPC server interface that supports function calling and tool use through a schema-based function registry. The system parses tool/function definitions into a registry, validates function calls against schemas, and integrates with the router's tool parsing pipeline to extract and execute function calls from model outputs. This enables building agent systems where the model can reliably call external tools with validated arguments.

Implements a comprehensive quantization system that supports multiple quantization schemes (FP8, FP4/MXFP4, INT8) with a pluggable quantization registry. The system includes quantization kernels optimized for each scheme, automatic quantization configuration based on model and hardware, and integration with the model loading pipeline. Quantized models run on lower-precision compute, reducing memory footprint and increasing throughput while maintaining output quality through careful calibration.

Processes multimodal inputs (text + images/videos) for vision-language models by encoding images through a vision encoder, then interleaving image embeddings with text tokens in the input sequence. The system maintains a registry of supported vision-language models (LLaVA, Qwen-VL, etc.) and their specific image encoding and interleaving strategies. This enables models to reason over both text and visual content in a unified forward pass.