Transforms slow PyTorch models into highly optimized CUDA/Triton kernels using automated multi-agent optimization.
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Forge is an MCP server that provides access to a powerful AI-driven optimization engine. It transforms PyTorch models into production-grade CUDA/Triton kernels, achieving significant inference speedups (up to 14x faster) while maintaining numerical correctness. By connecting any MCP-compatible AI coding agent, developers can submit PyTorch code for optimization or generate new kernels from natural language descriptions, leveraging 32 parallel AI agents and real datacenter GPU benchmarking.
Key Features
01Optimize existing PyTorch kernels into Triton/CUDA
02Utilizes 32 parallel AI swarm agents for optimization
03Smart detection for identifying GPU optimization opportunities
04Generate new optimized GPU kernels from natural language
05Benchmarks kernels on real datacenter GPUs for accuracy
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Use Cases
01Improving performance of complex PyTorch operations like attention or custom autograd functions
02Accelerating PyTorch model inference for production deployment
03Generating custom, highly optimized CUDA/Triton kernels from scratch
