Cerebras Systems

NVIDIA DGX Spark is a compact AI workstation from NVIDIA for local development, fine-tuning, and inference of generative-AI models. It is built around the NVIDIA GB10 Grace Blackwell Superchip, with 128 GB of unified memory and up to 1 petaflop of FP4 AI performance, and runs NVIDIA DGX OS.

Fireworks is an AI platform that provides training and inference tools to transform open models into domain-specific or specialized intelligence. The service offers capabilities for model training, fine-tuning and serving to support customized AI deployments.

Colibrì is a pure-C inference engine and open research platform for running large mixture-of-experts models on consumer and heterogeneous hardware without engine dependencies. It treats VRAM, system RAM, and storage as a unified multitier inference hierarchy, streaming expert weights from disk and coordinating placement, storage I/O, scheduling, kernels, speculation, and CPU/GPU overlap. The project provides chat, server, and web front ends, with support for CPU, CUDA, Metal, and dual-SSD streaming; its design rules prohibit silently changing model precision or router semantics when fast memory is insufficient.

Baseten is an inference platform and company that provides tools to deploy, serve, and scale open-source and custom AI models in production. It offers managed infrastructure and tooling for model hosting, APIs, and inference at scale.

WARP (Weight-Aware Runtime and Paging), formerly WASTE, is an embeddable inference engine written in dependency-free C by SQLiteAI. It runs large mixture-of-experts models beyond available RAM by keeping the shared model trunk in memory, streaming selected experts from NVMe or other fast storage, and using remaining RAM as a bounded expert cache. Its container layout gives each expert an aligned read; reads overlap computation, while a lookahead router prefetches experts for the next layer without changing the decisions made by the actual router. Experts use 3-bit residual vector quantization, while more sensitive shared weights use 4- or 8-bit storage. The project is designed for local inference of frontier-scale models such as Kimi K3 and GLM-5.3-Flash on consumer hardware.