TensorRT-LLM

vLLM is an open-source library and inference-serving engine for deploying large language models on GPUs and other hardware accelerators. Originally developed in UC Berkeley's Sky Computing Lab, it is maintained by a broad community and provides an OpenAI-compatible API server, as well as Anthropic Messages API and gRPC support. Its serving stack manages attention key-value memory with PagedAttention, continuous batching, chunked prefill, prefix caching, streaming generation, and disaggregated prefill, decode, and encode. It also supports speculative decoding, quantization, optimized attention and GEMM/MoE kernels, automatic kernel generation and graph-level transformations, structured outputs, tool calling, multiple decoding algorithms, and distributed inference through tensor, pipeline, data, expert, and context parallelism. The project integrates with Hugging Face models and supports decoder-only, mixture-of-experts, hybrid state-space, multimodal, embedding, retrieval, reward, and classification models. It can run across NVIDIA, AMD, Intel, and other supported accelerators, as well as x86, ARM, and PowerPC CPUs, and can be installed with uv or pip or built from source.

GuideLLM is an open-source platform for benchmarking and evaluating real-world LLM inference deployments. It simulates end-to-end interactions with OpenAI-compatible and vLLM-native servers, using real or synthetic text and multimodal datasets and configurable execution profiles such as synchronous, concurrent, throughput, constant-rate, Poisson, and sweep workloads. It measures SLO-oriented latency and token statistics, including time to first token, inter-token latency, and end-to-end behavior, then produces console, JSON, CSV, or HTML reports for deployment tuning, capacity planning, and regression tracking. The tool provides both a CLI and API, with multiprocessing, threading, asynchronous execution, and Hugging Face, file, synthetic, or custom data sources.

TensorRT is NVIDIA's inference-serving runtime for optimizing models for high throughput. In the cited context, it is used as a serving-runtime option when deciding whether to run inference on dedicated GPUs, alongside measurements of latency, throughput, precision, memory use, and workload performance.

Liquid Inference is a live auction exchange for LLM inference. Providers compete on price for each AI request, and the request is routed to the lowest offer that meets the user's rules and firm price cap. The service provides receipts and published benchmarks and is described as compatible with OpenAI and Anthropic APIs.

An open GitHub guide and configuration project by jamesob for running state-of-the-art large language models locally. It documents a multi-GPU system using PCIe switches so GPUs can communicate peer-to-peer, including hardware selection, BIOS bifurcation, link-speed and ASPM settings, kernel and GRUB parameters, ACS configuration, and GPU power limiting. The repository also provides Docker-based serving configurations, a local speech-to-text configuration, and a GPU peer-to-peer bandwidth and latency benchmark.