March 2026 monthly summary for HazyResearch/ThunderKittens. Delivered a README update documenting GEMM performance metrics across implementations, augmenting educational clarity and providing benchmarks for users. This work is linked to the commit that validates educational GEMM benchmarks.

August 2025 (2025-08) monthly summary for HazyResearch/ThunderKittens. Key feature delivered: GPU-Accelerated Linear Attention with a CUDA kernel and a Triton-based implementation, including a Makefile, test harness, and benchmarking suite. Correctness validated against PyTorch outputs; performance benchmarks conducted across configurations to measure speedups and efficiency. Major bugs fixed: none reported this month. Overall impact: enables scalable attention for longer sequences on GPUs, reducing latency and enabling larger models, which accelerates experimentation and product readiness. Technologies demonstrated: CUDA, Triton, Python, PyTorch, Makefiles, test automation, and benchmarking pipelines. Commits associated: f8b85e4c8a4a37cdc968ea8a19674d07acc4993d; 72391d964ec9aeca9f836cef49fa9c7548a92dbc.

June 2025: Launched the Educational CUDA Matmul Benchmark Suite in HazyResearch/ThunderKittens, organized into levels 01–08 to demonstrate progressively optimized matrix multiplication techniques. Delivered a Makefile and a reusable launch/benchmark framework, plus a README documenting optimization levels from basic loops to advanced approaches such as tensor cores and Tensor Matrix Arithmetic (TMA). This work provides a reusable, educational, and benchmarking-ready foundation to accelerate onboarding and data-driven performance tuning for CUDA kernels. No major bugs fixed this cycle; primary focus was feature delivery and documentation, reinforcing business value by enabling faster, reliable performance assessment and optimization.

April 2025 monthly summary for HazyResearch/ThunderKittens. Focused month delivering core model infrastructure, plugin integrations, and build stability improvements. Key outcomes include the Silu MLP core implementation with tests and tooling, a new attention reduction plug-in, and Llama CUH reduction integration with cleanup; substantial progress toward compiling with fewer errors; numerics improvements applied to existing pipelines. Scheduling structure work continued to enable scalable workflows, while repo hygiene and minor quality improvements supported maintainability. Business value: faster feature delivery, more reliable builds, and a cleaner codebase to enable rapid experimentation and deployment at scale.

March 2025 monthly summary for HazyResearch/ThunderKittens focusing on kernel data access and calculation correctness fixes. Implemented targeted corrections to data dimension accessors and function call syntax across FFT convolution and rotary kernel to ensure accurate data processing. The changes stabilized core kernels, reducing edge-case failures and improving reliability of downstream analytics.

February 2025 (2025-02) — ThunderKittens: Focused on delivering high-value features, stabilizing core APIs, and strengthening validation to accelerate DL workloads and reduce risk.

January 2025 performance highlights across two repos: delivered high-impact GPU kernel enhancements and research tooling to drive throughput, broaden GPU baseline coverage, and improve developer onboarding. Major work spans HazyResearch/ThunderKittens and ScalingIntelligence/KernelBench. Key deliverables include a scalable FP8 matrix-multiplication kernel with FP8/FP16 support, input scaling, and WGMMA integration, plus test generation and benchmarks; onboarding improvements and README polish to reduce contributor friction; and KernelBench enhancements including a few-shot learning baseline, CoT prompts for fuse_gelu, updated docstrings, and expanded H100 baseline coverage with Torch compile baselines. Collectively, these efforts increase computational throughput, enable faster experimentation on FP8 paths, broaden GPU-backend compatibility, and strengthen the foundation for future research and deployment.

November 2024 monthly summary for ThunderKittens: Delivered FP8-first performance improvements focusing on WMMA/WGMA integration and memory-layout optimizations. Implemented FP8 type definitions, runtime packing changes, and E5M2 support, with validation checks and RTX 4090 compatibility. Enhanced FP8 GEMM kernels and WMMA/WGMA integration, including transposed MMA support and sizing improvements. Reworked memory paths by migrating global memory to shared memory and optimizing IO through group-shared to register IO and warp-level IO enhancements. Introduced checkpoint kernel and GEMM baselines to support fault tolerance and performance comparisons. Strengthened stability and quality with regression-proofing PyTorch builds, MMA unit test fixes, expanded FP8 unit tests, and documentation updates.

Monthly summary for 2024-10 (HazyResearch/ThunderKittens): Key feature delivered was Community Engagement and Documentation Enhancements. Implemented a Demos section, Learn more and get involved guidance, and enhanced READMEs with references to the Discord channel, blog link, and additional Discord invite links to improve onboarding and community participation. Major bugs fixed: none reported for this period. Overall impact: improved onboarding, increased user engagement and contributor participation, and better discoverability of community channels. Technologies/skills demonstrated: documentation engineering, markdown/readme design, community tooling integration, and cross-repo documentation consistency.