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Forge Agent

Swarm Agents That Turn Slow PyTorch Into Fast CUDA/Triton Kernels

opening dashboard... 0%

Drop .py file here

Target GPUs

Output

Iterations

Early Stopat

1credit

1 GPU•10 iter•stop at 2x

⌘+↵

Abstract

Forge transforms PyTorch models into production-grade CUDA/Triton kernels through automated multi-agent optimization. Using 32 parallel AI agents with inference-time scaling, it achieves up to 14× faster inference than torch.compile(mode='max-autotune-no-cudagraphs') while maintaining 100% numerical correctness.

Input

PyTorch

nn.Module / fn

CUDA

.cu / .py

Custom Prompt

describe your kernel

Swarm Agents

Output

PyTorch + CUDA

native kernels

3.8x

PyTorch + Triton

JIT-compiled

2.9x

drop-in replacement

Optimize existing code or generate new kernels from a prompt.

1What is Forge?

Traditional PyTorch models suffer from suboptimal GPU utilization due to generic kernel implementations. Manual optimization requires deep CUDA expertise and weeks of engineering time per layer. Forge eliminates this bottleneck through automated kernel generation powered by competitive multi-agent search.

The system deploys 32 parallel Coder+Judge agent pairs that compete to discover optimal kernel implementations. Each agent explores different optimization strategies: tensor core utilization, memory coalescing, register blocking, shared memory tiling, and kernel fusion. Agents are powered by a fine-tuned NVIDIA Nemotron 3 Nano 30B model generating 250k tokens/second, enabling comprehensive search in minutes rather than hours.

Forge targets the full optimization stack—from layer-wise kernels to model-wide fusion patterns. The system works with any PyTorch model.

4Pricing

PAY AS YOU GO

Agent Credits

Credit Refund if We Don't Beat torch.compile(mode='max-autotune-no-cudagraphs')

1102550100

10 credits

$150.00$112.50

-$37.50 saved

BUY

Free trialon 1 kernel optimization•No credit card required

✓Datacenter GPUs Access (B200, H100, H200)
✓Inference Time Scaling with NVIDIA Nemotron 3 Nano 30B (250k tokens/sec)
✓32 Parallel Swarm Agents with Coder+Judge Pattern
✓Advanced Kernel Database Retrieval
✓Any PyTorch Model → All Layers Optimized

Abstract

Input

PyTorch

nn.Module / fn

CUDA

.cu / .py

Custom Prompt

describe your kernel

Swarm Agents

Output

PyTorch + CUDA

native kernels

3.8x

PyTorch + Triton

JIT-compiled

2.9x

drop-in replacement

Optimize existing code or generate new kernels from a prompt.

1What is Forge?

Forge targets the full optimization stack—from layer-wise kernels to model-wide fusion patterns. The system works with any PyTorch model.

2How It Works

Figure 2: Multi-agent swarm optimization on NVIDIA B200. Contour lines represent kernel latency landscape across block size, thread count, and memory layout parameter space. 32 independent Coder+Judger agent pairs compete to find optimal kernel configurations. Only 3 agents successfully navigate to the global optimum at 8.24ms, while others converge to local minima at 14.76ms and 19.32ms or remain in suboptimal regions.

3Results

Benchmark results comparing Forge against torch.compile on NVIDIA B200.

torch.compile(mode='max-autotune-no-cudagraphs')

Forge

100ms80ms60ms40ms20ms0

5.16x

Llama-3.1-8B

meta-llama/Llama-3.1-8B-Instruct. GQA + RoPE + SwiGLU fused kernels.

torch: 42.3msForge: 8.2ms

4.23x

Qwen2.5-7B

Qwen/Qwen2.5-7B-Instruct. Sliding window attention + fused MLP.

torch: 38.5msForge: 9.1ms

3.38x

Mistral-7B

mistralai/Mistral-7B-Instruct-v0.3. Grouped-query attention optimization.

torch: 35.2msForge: 10.4ms

2.75x

Phi-3-mini

microsoft/Phi-3-mini-4k-instruct. Long context RoPE + flash attention.

torch: 18.7msForge: 6.8ms

2.87x

SDXL UNet

stabilityai/stable-diffusion-xl. Cross-attention + conv fused kernels.

torch: 89.4msForge: 31.2ms

2.63x

Whisper-large

openai/whisper-large-v3. Encoder-decoder attention optimization.

torch: 52.1msForge: 19.8ms

2.43x

BERT-large

google-bert/bert-large-uncased. Bidirectional attention + FFN fusion.

torch: 12.4msForge: 5.1ms

Figure 2: Latency comparison in milliseconds. Gray bars show torch.compile, orange bars show Forge.

4Pricing

PAY AS YOU GO

Agent Credits

Credit Refund if We Don't Beat torch.compile(mode='max-autotune-no-cudagraphs')

1102550100

10 credits

$150.00$112.50

-$37.50 saved

BUY

Free trialon 1 kernel optimization•No credit card required

✓Datacenter GPUs Access (B200, H100, H200)
✓Inference Time Scaling with NVIDIA Nemotron 3 Nano 30B (250k tokens/sec)
✓32 Parallel Swarm Agents with Coder+Judge Pattern
✓Advanced Kernel Database Retrieval
✓Any PyTorch Model → All Layers Optimized