Swarm Agents That Turn Slow PyTorch Into Fast GPU Kernels
Forge Agent is an AI-powered GPU kernel optimization tool developed by RightNow AI that transforms slow PyTorch models into production-grade CUDA/Triton kernels. Using a swarm of 32 parallel AI agents with inference-time scaling, Forge automatically discovers optimal kernel configurations by exploring tensor core utilization, memory coalescing, shared memory tiling, and kernel fusion simultaneously. Powered by NVIDIA Nemotron 3 Nano 30B running at 250,000 tokens per second, Forge achieves up to 14x faster inference than torch.compile while maintaining 100% numerical correctness.
Swarm Agent Optimization - 32 parallel Coder+Judge AI agents compete to discover optimal kernel configurations simultaneously
Multi-GPU Support - Target optimization for NVIDIA B200, H200, H100, L40S, A100, L4, A10, and T4 GPUs
Dual Output Formats - Generate either native CUDA kernels or Triton JIT-compiled kernels as needed
Performance Guarantee - 100% refund guarantee if Forge cannot beat torch.compile performance
Drop-in Replacement - Output kernels maintain the exact same API as original PyTorch code for seamless integration
Inference-Time Scaling - Powered by NVIDIA Nemotron 3 Nano 30B at 250k tokens/sec for rapid optimization
Automated Kernel Fusion - Automatically identifies and fuses operations for maximum GPU utilization
#1 LLM Inference Optimization - Accelerate large language model inference by optimizing attention mechanisms, RoPE embeddings, and MLP layers, achieving up to 5.16x speedup on models like Llama-3.1-8B
#2 Diffusion Model Acceleration - Speed up image generation workflows by optimizing UNet cross-attention and convolution kernels in models like Stable Diffusion XL
#3 Speech Recognition Enhancement - Optimize encoder-decoder attention in audio transcription models like Whisper for faster real-time processing
#4 Production ML Deployment - Convert research PyTorch models into production-ready GPU kernels without requiring CUDA expertise
#5 GPU Resource Efficiency - Reduce cloud computing costs by maximizing GPU utilization through optimized memory coalescing and tensor core usage
What makes Forge different from torch.compile? Forge uses multi-agent swarm optimization with 32 parallel AI agents that explore kernel configurations torch.compile cannot reach. Benchmarks show 2.4x to 5.2x improvements over torch.compile's max-autotune mode across production models.
Do I need CUDA programming experience to use Forge? No, Forge is designed to automate the entire kernel optimization process. Simply upload your PyTorch code and the swarm agents handle all CUDA/Triton optimization automatically.
What happens if Forge cannot improve my model's performance? Forge offers a 100% refund guarantee if the optimized kernels do not outperform torch.compile on your specific workload.
How long does the optimization process take? With 250k tokens/sec inference speed, most optimizations complete in minutes rather than the hours or weeks manual CUDA optimization would require.
Are the optimized kernels production-ready? Yes, Forge outputs drop-in replacement kernels that maintain 100% numerical correctness while providing significant performance improvements.
