MCP server for the ParalleliX Compute API that lets you offload parallel inference tasks (bulk classify, extract, summarize, etc.) to a distributed network of open-source models.
Unified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.
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Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Enables building and running custom LLM benchmarks with multi-judge evaluation, supporting GUI, MCP client, and CLI usage for ranked, auditable results.
InferBench's MCP server lets coding agents run, serve and benchmark local LLMs (text + image, llama.cpp + Stable Diffusion) on your own hardware on demand — measuring real tokens/sec and picking the optimal quant for your GPU from a 124-model catalog. Local-first, no cloud required.