HazyResearch/ThunderKittens
Mar 2025 – Feb 2026
10 Months active
Languages Used
C++C/C++CUDAMakefileShellCudaJAXJupyter Notebook
Technical Skills
AssemblyBuild SystemsC++C++ MetaprogrammingC++ Template MetaprogrammingC++ Templates
StuartSul
PROFILE
Stuartsul
Stuart contributed to the HazyResearch/ThunderKittens repository, building distributed GPU kernels and infrastructure for high-performance multi-GPU machine learning workloads. He engineered core features such as asynchronous matrix multiplication, collective communication primitives, and advanced quantization support, leveraging C++, CUDA, and Python. His work included deep integration with PyTorch, robust memory management, and cross-architecture compatibility, enabling scalable training and efficient resource utilization. Stuart emphasized maintainability through extensive testing, code refactoring, and documentation, while addressing reliability with synchronization primitives and race condition fixes. The depth of his engineering enabled faster iteration, improved throughput, and a smoother developer experience for large-scale model development.
Overall Statistics
Feature vs Bugs
74%Features
Repository Contributions
437Total
Bugs
60
Commits
437
Features
167
Lines of code
142,008
Activity Months10
Your Network
23 people
Work History
February 2026
19 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for HazyResearch/ThunderKittens: Delivered high-impact GEMM kernel enhancements with configurability and advanced tiling, and expanded CUDA data movement capabilities. Implemented a 5-stage pipeline, Nb=128 support, loop unrolling, prefetching, and synchronization optimizations to boost throughput and scalability on CUDA-enabled hardware. Added a tensor_commit utility and generic non-tensor TMA transfers, while simplifying synchronization primitives by removing unnecessary fences. These changes improve performance, reliability, and deployment readiness across CUDA GPUs and lay groundwork for continued tuning.
19 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for HazyResearch/ThunderKittens: Delivered high-impact GEMM kernel enhancements with configurability and advanced tiling, and expanded CUDA data movement capabilities. Implemented a 5-stage pipeline, Nb=128 support, loop unrolling, prefetching, and synchronization optimizations to boost throughput and scalability on CUDA-enabled hardware. Added a tensor_commit utility and generic non-tensor TMA transfers, while simplifying synchronization primitives by removing unnecessary fences. These changes improve performance, reliability, and deployment readiness across CUDA GPUs and lay groundwork for continued tuning.
February 2026
January 2026
61 Commits • 30 Features
Jan 1, 2026January 2026 performance summary for HazyResearch/ThunderKittens: - Delivered ThunderKittens 2.0 release with initial 2.0 changes, enabling a major milestone for feature parity and platform readiness. Commit: e463ed89e2f7cc145022404e80faade95a126f09. - Fixed critical NVFP4 quantization bug and introduced enhancements including 16x16 2D quantization support, improving accuracy and performance on quantized models. Commits: eb3d25b6ff533b3d5cddc1211b444cef6a536ded; d82f44eb132c777a80905a945c8061c9a20dd26b; 02cba75638b56ffc31401511a5ad98314208c71c. - Expanded performance and footprint with quantization and kernel optimizations (block tiling, kernel-level improvements) to boost throughput on dense workloads. Commits: e69bd9f6807a1f6982fc5ed6d94d00822abe3688; c0e9e9c1c3dec633ffc38b702d95cfc7c68b90d9. - Accelerated compute on FP8/GEMM with new B200 FP8 GEMM kernels and benchmark alignment (RS, BF16 AG+GEMM, FP8 AG+GEMM) and aligned FP8 benchmarks with BF16. Commits: cc37f9abd4e116600dd68f89d5473ce16ec8c777; 121829b8b073fee4a8aa51cca497f6f38ce00d86; d6a6960c54563c5dbfc7b569c3e46469113ac5ae. - Broadened hardware support and tooling: B300 GPU support, Hopper compatibility, SM count utility, launch/config utilities, and substantial PDL integration and utilities enabling earlier PDL arrivals and cross-component usage. Commits include b8337f56482560de1cc9127cad5fd25a2c34ad21; d5f5b4df279900492679c145e93531a1863ce82d; b3aadb4ecd1e3957c739a98d94127313b392bf5d; a495eeb0e84af3cc0c2c6bc33ab9b115f1befc32; 2c69999757fe334f82f1a173e58d59de4c20c55b; db5959b256db789a8bda0ef441c75a6b8c0656f4; a3d2e2abff33aa48161f3a4514b261cd954736ac; 84cf5c4c53604713bf347fb1c4dcbdfcdf7145be; f4028594c8e76fa5ac44fcdae2c3410435120d13. - Reliability, lifecycle and readability improvements: memory lifecycle separation for TMEM, synchronization management, removal of register allocation to simplify resource management, and documentation/readability improvements (README updates, added comments). Commits: b7f1e927b0923f0871cee64b9799b5aa7f647a28; dc6d67c550040e04b1a46d6478528ec53dff4877; 55d6f8fe0af1d863319ca1495d0afb0e6ec1caa7; 88d60362ec16bd3d0686581caf5518f0a6d28aa1; 7b727810bdd0a168e208139543b6398d54d00266; 82628bee3210490bd4c3e9d531a6d8240497e522.
January 2026
61 Commits • 30 Features
Jan 1, 2026January 2026 performance summary for HazyResearch/ThunderKittens: - Delivered ThunderKittens 2.0 release with initial 2.0 changes, enabling a major milestone for feature parity and platform readiness. Commit: e463ed89e2f7cc145022404e80faade95a126f09. - Fixed critical NVFP4 quantization bug and introduced enhancements including 16x16 2D quantization support, improving accuracy and performance on quantized models. Commits: eb3d25b6ff533b3d5cddc1211b444cef6a536ded; d82f44eb132c777a80905a945c8061c9a20dd26b; 02cba75638b56ffc31401511a5ad98314208c71c. - Expanded performance and footprint with quantization and kernel optimizations (block tiling, kernel-level improvements) to boost throughput on dense workloads. Commits: e69bd9f6807a1f6982fc5ed6d94d00822abe3688; c0e9e9c1c3dec633ffc38b702d95cfc7c68b90d9. - Accelerated compute on FP8/GEMM with new B200 FP8 GEMM kernels and benchmark alignment (RS, BF16 AG+GEMM, FP8 AG+GEMM) and aligned FP8 benchmarks with BF16. Commits: cc37f9abd4e116600dd68f89d5473ce16ec8c777; 121829b8b073fee4a8aa51cca497f6f38ce00d86; d6a6960c54563c5dbfc7b569c3e46469113ac5ae. - Broadened hardware support and tooling: B300 GPU support, Hopper compatibility, SM count utility, launch/config utilities, and substantial PDL integration and utilities enabling earlier PDL arrivals and cross-component usage. Commits include b8337f56482560de1cc9127cad5fd25a2c34ad21; d5f5b4df279900492679c145e93531a1863ce82d; b3aadb4ecd1e3957c739a98d94127313b392bf5d; a495eeb0e84af3cc0c2c6bc33ab9b115f1befc32; 2c69999757fe334f82f1a173e58d59de4c20c55b; db5959b256db789a8bda0ef441c75a6b8c0656f4; a3d2e2abff33aa48161f3a4514b261cd954736ac; 84cf5c4c53604713bf347fb1c4dcbdfcdf7145be; f4028594c8e76fa5ac44fcdae2c3410435120d13. - Reliability, lifecycle and readability improvements: memory lifecycle separation for TMEM, synchronization management, removal of register allocation to simplify resource management, and documentation/readability improvements (README updates, added comments). Commits: b7f1e927b0923f0871cee64b9799b5aa7f647a28; dc6d67c550040e04b1a46d6478528ec53dff4877; 55d6f8fe0af1d863319ca1495d0afb0e6ec1caa7; 88d60362ec16bd3d0686581caf5518f0a6d28aa1; 7b727810bdd0a168e208139543b6398d54d00266; 82628bee3210490bd4c3e9d531a6d8240497e522.
November 2025
3 Commits • 2 Features
Nov 1, 2025Monthly summary for 2025-11 focused on ThunderKittens repo work items and impact.
3 Commits • 2 Features
Nov 1, 2025Monthly summary for 2025-11 focused on ThunderKittens repo work items and impact.
November 2025
October 2025
11 Commits • 3 Features
Oct 1, 2025October 2025: Delivered cross-hardware performance portability and core multi-GPU capabilities for ThunderKittens, with stability and developer experience improvements. Key deliverables include FP8e8m0 support on Blackwell, Hopper-specific tuning to avoid performance regressions, robust inter-SM/inter-GPU synchronization primitives, tiled multi-GPU reductions, and host-launch compute/communication templates, along with memory-safety fixes and repository hygiene updates. These changes reduce regression risk on Hopper while unlocking Blackwell performance benefits, enabling scalable multi-GPU workloads and clearer API usage.
October 2025
11 Commits • 3 Features
Oct 1, 2025October 2025: Delivered cross-hardware performance portability and core multi-GPU capabilities for ThunderKittens, with stability and developer experience improvements. Key deliverables include FP8e8m0 support on Blackwell, Hopper-specific tuning to avoid performance regressions, robust inter-SM/inter-GPU synchronization primitives, tiled multi-GPU reductions, and host-launch compute/communication templates, along with memory-safety fixes and repository hygiene updates. These changes reduce regression risk on Hopper while unlocking Blackwell performance benefits, enabling scalable multi-GPU workloads and clearer API usage.
September 2025
35 Commits • 23 Features
Sep 1, 2025September 2025 performance summary for HazyResearch/ThunderKittens. Delivered a broad set of features and stability improvements across MXFP8 data path, IPC/memory management, and multi-GPU coordination, complemented by production tooling and educational assets. Key outcomes include enabling FP8E8M0 data type, per-warp register allocation, memory-aware KittensBroker ownership, and a PGL refactor with multimem abstractions to improve memory performance and scalability. Expanded kernel coverage for collective operations (all-reduce, all-gather, reduce-scatter, all-to-all), multi-GPU synchronization, and launch bounds configurability, alongside PyTorch utilities and production-ready Makefiles. Fixed critical reliability issues (KittensBroker ownership/IPCP pointer revamp, removal of deprecated sync manager, invalid examples, and race condition in concurrent execution). Overall impact: faster training iterations, improved scalability and reliability for distributed workloads, and a smoother developer experience for PyTorch-based workflows.
35 Commits • 23 Features
Sep 1, 2025September 2025 performance summary for HazyResearch/ThunderKittens. Delivered a broad set of features and stability improvements across MXFP8 data path, IPC/memory management, and multi-GPU coordination, complemented by production tooling and educational assets. Key outcomes include enabling FP8E8M0 data type, per-warp register allocation, memory-aware KittensBroker ownership, and a PGL refactor with multimem abstractions to improve memory performance and scalability. Expanded kernel coverage for collective operations (all-reduce, all-gather, reduce-scatter, all-to-all), multi-GPU synchronization, and launch bounds configurability, alongside PyTorch utilities and production-ready Makefiles. Fixed critical reliability issues (KittensBroker ownership/IPCP pointer revamp, removal of deprecated sync manager, invalid examples, and race condition in concurrent execution). Overall impact: faster training iterations, improved scalability and reliability for distributed workloads, and a smoother developer experience for PyTorch-based workflows.
September 2025
August 2025
1 Commits
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key deliverables and reliability improvements in distributed synchronization.
August 2025
1 Commits
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key deliverables and reliability improvements in distributed synchronization.
July 2025
2 Commits • 1 Features
Jul 1, 2025Month 2025-07 — Delivered stability fixes and cross-architecture GPU support for ThunderKittens. Key work includes a PGL multicast memory alignment fix preventing device crashes and a generalized PGL all-reduce example kernel with multi-GPU support, replacing H100-specific setup. These efforts improve reliability, portability, and performance of multi-GPU workloads across devices.
2 Commits • 1 Features
Jul 1, 2025Month 2025-07 — Delivered stability fixes and cross-architecture GPU support for ThunderKittens. Key work includes a PGL multicast memory alignment fix preventing device crashes and a generalized PGL all-reduce example kernel with multi-GPU support, replacing H100-specific setup. These efforts improve reliability, portability, and performance of multi-GPU workloads across devices.
July 2025
June 2025
27 Commits • 8 Features
Jun 1, 2025June 2025 performance summary for HazyResearch/ThunderKittens: Delivered extensive test suite integration, deepened PGL/PyTorch compatibility with multi-GPU support, ported core ops and example kernels to the updated framework, and instituted safety and quality improvements that stabilize and accelerate enterprise workflows. The work enhances CI reliability, enables broader hardware and framework compatibility (PyTorch/H100/Megakernel), and improves maintainability through code hygiene and clearer error reporting. These changes lay groundwork for faster model iteration, scalable training, and easier onboarding for new contributors.
June 2025
27 Commits • 8 Features
Jun 1, 2025June 2025 performance summary for HazyResearch/ThunderKittens: Delivered extensive test suite integration, deepened PGL/PyTorch compatibility with multi-GPU support, ported core ops and example kernels to the updated framework, and instituted safety and quality improvements that stabilize and accelerate enterprise workflows. The work enhances CI reliability, enables broader hardware and framework compatibility (PyTorch/H100/Megakernel), and improves maintainability through code hygiene and clearer error reporting. These changes lay groundwork for faster model iteration, scalable training, and easier onboarding for new contributors.
April 2025
189 Commits • 65 Features
Apr 1, 2025April 2025 (ThunderKittens) achievements: established a baseline minimal working version and built out multi-PGL workflows with TMA integration, introduced asynchronous IO and prefetch/write paths, added axes/dtypes support and tiles, and expanded testing for cross-framework correctness. A broad set of bug fixes improved stability, performance, and correctness across device handling, synchronization, and macro/workflow correctness. Business value is evident in improved throughput, reliability, and scalability for multi-GPU pipelines and heterogeneous workloads.
189 Commits • 65 Features
Apr 1, 2025April 2025 (ThunderKittens) achievements: established a baseline minimal working version and built out multi-PGL workflows with TMA integration, introduced asynchronous IO and prefetch/write paths, added axes/dtypes support and tiles, and expanded testing for cross-framework correctness. A broad set of bug fixes improved stability, performance, and correctness across device handling, synchronization, and macro/workflow correctness. Business value is evident in improved throughput, reliability, and scalability for multi-GPU pipelines and heterogeneous workloads.
April 2025
March 2025
89 Commits • 33 Features
Mar 1, 2025March 2025 monthly performance summary for HazyResearch/ThunderKittens: Delivered foundational platform utilities and layout scaffolding to improve usability and parallelism; introduced asynchronous TP matmul support to enable overlapped computation; established a comprehensive all-reduce kernel with testing scaffolding for reduction ops and vector testing; completed PGL core integration with multi-GPU testing infrastructure, improved memory management, and added test scaffolding; expanded multimem.red support with associated vector and packed scalar tests; pursued code quality and modernization through constexpr adoption and readability refinements. These efforts collectively increased feature velocity, improved stability, and enhanced multi-GPU readiness while maintaining a strong emphasis on maintainability and code health.
March 2025
89 Commits • 33 Features
Mar 1, 2025March 2025 monthly performance summary for HazyResearch/ThunderKittens: Delivered foundational platform utilities and layout scaffolding to improve usability and parallelism; introduced asynchronous TP matmul support to enable overlapped computation; established a comprehensive all-reduce kernel with testing scaffolding for reduction ops and vector testing; completed PGL core integration with multi-GPU testing infrastructure, improved memory management, and added test scaffolding; expanded multimem.red support with associated vector and packed scalar tests; pursued code quality and modernization through constexpr adoption and readability refinements. These efforts collectively increased feature velocity, improved stability, and enhanced multi-GPU readiness while maintaining a strong emphasis on maintainability and code health.
Activity
Loading activity data...
Quality Metrics
Correctness89.2%
Maintainability85.0%
Architecture85.6%
Performance83.0%
AI Usage20.8%
Skills & Technologies
Programming Languages
CC++C/C++CUDACudaGitJAXJupyter NotebookMakefileMarkdown
Technical Skills
AssemblyAsynchronous OperationsAsynchronous ProgrammingAsynchronous operationsAttention MechanismsBackend DevelopmentBenchmarkingBug FixingBuild SystemsBuild systemsC programmingC++C++ DevelopmentC++ Extension DevelopmentC++ Metaprogramming
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline