Forge Mcp Server
Let your coding agent compile PyTorch hot paths into CUDA or Triton kernels and benchmark them on datacenter GPUs without hand-writing GPU code.
Overview
Forge MCP Server is a Build-phase MCP server that helps agents convert PyTorch workloads into faster CUDA/Triton kernels on datacenter GPUs.
What is this MCP server?
- stdio MCP server (npm @rightnow/forge-mcp-server v1.0.2) for agent-driven kernel workflows
- Targets real datacenter GPUs with CUDA/Triton output from PyTorch-oriented workflows
- Marketed up to 14x speedup versus naive PyTorch paths on suitable workloads
- GitHub source at RightNow-AI/forge-mcp-server for audit and version pinning
- Fits agent-tooling stacks where the model writes and iterates on kernel code
- Server schema version 1.0.2 on npm identifier @rightnow/forge-mcp-server
- stdio transport only in published server.json packages entry
- Publisher documents up to 14x speedup on real datacenter GPUs for qualifying workloads
Community signal: 13 GitHub stars.
What problem does it solve?
PyTorch prototypes often ship too slowly on GPU because writing and tuning CUDA or Triton by hand is slow and error-prone for a solo builder.
Who is it for?
Indie ML engineers and agent-first teams already on PyTorch who need datacenter GPU performance without a dedicated CUDA specialist on payroll.
Skip if: Non-PyTorch stacks, CPU-only apps, or builders who only need generic cloud provisioning with no custom kernel work.
What do I get? / Deliverables
After you register the stdio MCP server, your agent can drive kernel generation and optimization loops so inference or training paths get closer to production throughput before you deploy.
- Agent-callable MCP tools for PyTorch-to-kernel optimization workflows
- CUDA or Triton kernel candidates aligned to your PyTorch modules
- Iterative performance tuning context kept inside the coding agent session
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Journey fit
Kernel generation and GPU tuning sit in the build phase when you are shipping model inference or training code that must run fast in production. Backend placement matches server-side ML workloads, custom ops, and inference pipelines rather than UI or distribution work.
How it compares
GPU kernel acceleration MCP for PyTorch, not a general cloud IaC or database connector.
Common Questions / FAQ
Who is Forge MCP Server for?
Solo and indie builders shipping PyTorch models or custom ops who want their AI coding agent to help produce CUDA/Triton kernels and chase speedups on real GPUs.
When should I use Forge MCP Server?
Use it during the build phase when profiling shows GPU bottlenecks and you are ready to iterate on generated kernels before production deploy.
How do I add Forge MCP Server to my agent?
Install the npm package @rightnow/forge-mcp-server (v1.0.2), add a stdio MCP entry in Claude Code, Cursor, or your client's config, and restart the client so tools load over stdin/stdout.