Mini Sglang

Large Language Model Serving - Provides a high-throughput inference server for hosting and serving large language models.

LLM Inference Servers - Acts as a production-ready inference server for hosting large language models with high throughput.

Prefix Cache Reuse - Eliminates redundant computations by sharing and reusing key-value caches for common request prefixes.

Prefill Phase Optimizations - Implements chunked prefill execution to maintain a constant memory ceiling during initial sequence processing.

Tensor-Parallel Inference Distributions - Distributes model weights across multiple GPUs using tensor parallelism to increase memory and throughput.

OpenAI-Compatible Model Servers - Serves as an OpenAI-compatible inference server for hosting large language models across a network.

Prefix Caching - Reuses computed key-value caches for shared request prefixes to eliminate redundant calculations.

Tensor Parallelism - Distributes large model workloads across multiple GPUs using tensor parallelism to increase memory and computation speed.

KV Cache Management - Manages and tracks memory for key-value tensors to enable efficient retrieval of computed sequence states.

OpenAI-Compatible APIs - Exposes a standardized OpenAI-compatible API for seamless integration with existing LLM toolchains.

Inference Benchmarking Tools - Provides a local batch processing engine for conducting model ablation studies and inference performance tests.

Offline Inference Engines - Includes a local batch processing engine for offline model execution and performance ablation studies.

Inference Batching - Provides a local batch processing engine to maximize hardware utilization for offline benchmarking.

Long-Context Sequence Processors - Splits long input sequences into smaller chunks during prefill to prevent peak memory spikes.

Inference Engines - Lightweight, high-performance inference framework for LLMs.

Model Serving & Deployment - Offers a lightweight serving framework for LLMs.

sgl-projectmini-sglang