🗺️ TileGym Kernel Roadmap & Contribution Guide

Welcome to the TileGym roadmap! We use this page to provide transparency into our development progress and to invite the community to help us build the next generation of tile-based high-performance kernels.

1. Current Support Status

1.1 Operator Support

The following table tracks the support status for various operators.

Category	Operator	Forward	Backward
Linear Algebra	MatMul	✅ Available	N/A
Linear Algebra	Batch MatMul (BMM)	✅ Available	📅 Planned
Linear Algebra	Grouped GEMM	✅ Available	N/A
Linear Algebra	FP8 Quantized MatMul	🚧 WIP (Internal)	N/A
Linear Algebra	Split-K Reduction	✅ Available	N/A
Attention	Attention	✅ Available	🧪 Experimental
Attention	Flash Decode	✅ Available	N/A
Attention	Attention Sink Decode	✅ Available	N/A
Attention	Attention Sink	✅ Available	N/A
Attention	Autoregressive Flash Attention	🚧 WIP (Internal)	N/A
Attention	Flex Attention	📅 Planned	N/A
Attention	Multi-Head Compression (MHC)	✅ Available	N/A
Attention	Multi-Latent Attention (MLA)	✅ Available	N/A
Attention	MLA Decoding	✅ Available	N/A
Attention	MLA Decoding Split KV	✅ Available	N/A
Normalization	RMS Normalization	✅ Available	✅ Available
Normalization	Layer Normalization Legacy	✅ Available	📅 Planned
Normalization	Cache Layer Normalization	🚧 WIP (Internal)	🚧 WIP (Internal)
Normalization	Group Normalization	📅 Planned	N/A
Activation	SiLU and Mul	✅ Available	🧪 Experimental
Activation	SwiGLU	✅ Available	🧪 Experimental
Activation	Dropout	✅ Available	N/A
Activation	Softmax	✅ Available	🚧 WIP (Internal)
Fused Operations	Linear + Activation + Linear	🚧 WIP (Internal)	🚧 WIP (Internal)
Fused Operations	Linear + Bias + Activation	🚧 WIP (Internal)	🚧 WIP (Internal)
Fused Operations	Linear + Elementwise	🚧 WIP (Internal)	N/A
Fused Operations	Linear + GLU Activation + Linear	🚧 WIP (Internal)	📅 Planned
Mixture of Experts	MoE	✅ Available	N/A
Mixture of Experts	MoE Align Block	✅ Available	N/A
Positional Encoding	Rotary Position Embedding (RoPE)	✅ Available	✅ Available
Tensor Manipulation	Concatenation	🚧 WIP (Internal)	N/A
Tensor Manipulation	Transpose	🚧 WIP (Internal)	N/A
Signal Processing	Fast Fourier Transform (FFT)	🚧 WIP (Internal)	N/A
Convolution	Convolution	📅 Planned	📅 Planned
Loss Functions	Cross Entropy	🧪 Experimental	📅 Planned
Embedding	BERT Embeddings	🚧 WIP (Internal)	N/A
Optimizer	Fused Adam	📅 Planned	N/A
Pointwise	Squares	📅 Planned	N/A

1.2 E2E Model Support

The following table tracks the support status for various models.

Model	Status	Notes
LLaMA-3.1-8B	✅ Available	Tested on B200
DeepSeek-V2-Lite-Chat	✅ Available	Tested on B200
Qwen2-7B	✅ Available	Tested on B200
Qwen3.5-7B	✅ Available	Tested on B200
Gemma-3-4B-IT	✅ Available	Tested on B200
GPT-OSS	✅ Available	Tested on B200
Mistral-7B-Instruct-v0.3	✅ Available	Tested on B200
Phi-3-mini-4k-instruct	✅ Available	Tested on B200
OLMo-3-1025-7B	✅ Available	Tested on B200
More LLM models	🙋 Help Wanted

1.3 Kernel Library Support

The following table tracks the support status for various kernel libraries.

Library	Status	Notes
Flashinfer	🚧 WIP (Internal)
Tokamax	🚧 WIP (Internal)
Flaggems	🚧 WIP (Internal)
Other Libraries	📅 Planned	We welcome suggestions on which repositories you'd like to see cuTile performance in

Status Definitions:

• ✅ Available: Fully tested, performance optimized, and ready for production use.
• 🧪 Experimental: Functional and available, but carries the @experimental_kernel tag — not yet fully performance-validated. Community feedback welcome.
• 🚧 WIP (Internal): Currently being developed by the NVIDIA team. (Internal development is active; we recommend waiting for our PR to avoid conflicts).
• 📅 Planned: On our radar for future development. We are open to design discussions.
• 🙋 Help Wanted: We would love to have this, but don't have the bandwidth yet. Community contributions are highly encouraged!

2. Contribution Opportunities

We are actively looking for contributors to help with the following strategic areas:

🚀 Kernel Implementations (High Priority)

Optimize Existing Kernels

Make existing kernels run faster. Our internal optimization efforts currently focus on B200. If you discover optimizations that can make kernels faster, we welcome your contributions. You can choose to add tuning configs for specific architectures. However, if you make changes to the kernel itself, we will internally test whether your optimizations cause performance regressions on all covered GPUs.

Submit New Kernels

We welcome contributions of any new kernels, especially kernels required by new models. Before you start implementing, please check existing kernels in the repository, review our roadmap, and search through open issues to ensure that no one else is already working on the same kernel.

🔗 E2E Model Support

New Model Integration: Help us support more LLM models (e.g., Mixtral, Llama 4 and beyond).

Model Optimization: Performance tuning and optimization for existing model support.

3. How to Contribute

For detailed contribution guidelines, please refer to CONTRIBUTING.md.

If you want to contribute a new kernel or claim a Help Wanted task:

1. Review Existing Code: Check tilegym/ops/cutile (e.g., the GEMM implementation) to understand our DSL and coding standards.

2. Submit a PR: Directly open a pull request with your implementation. Your PR description must include:

   • Performance profiling data comparing against baseline implementations (e.g., torch, cuBLAS, flashinfer, or Triton).
   • Unit tests covering various shapes.

For E2E Model Support: If your contribution involves end-to-end model support and will take a significant amount of time, please open an issue first to discuss your approach and let us know that you are working on it. This helps us coordinate efforts and avoid duplicate work.

If you meet any problems, please [Open an Issue] to let us know. Your feedback helps us prioritize our internal roadmap!