CUTLASS 4.3.5

CuTe DSL

• Bug fixing and improvements
  • Fixed the unexpected CPU overhead issue introduced by 4.3.4
• Update copyright to 2026.

CUTLASS C++

• Update copyright to 2026.
• Use CUDA Driver Get Version Runtime APIs Rather than Driver APIs.

CUTLASS 4.3.4

CuTe DSL

• New features

  • Added PDL support along with example Kernel launch with Programmatic Dependent Launch

• Bug fixing and improvements

  • Fixed a frame refcnt issue with cuda graph
  • Enhancement for tvm-ffi AoT case for earlier module unload
  • Fixed order issue in make_smem_layout_a in utils/hopper_helpers.py

CUTLASS C++

• Work around a driver TMA descriptor related bug which will cause occasionally errors on Blackwell when the tensor's backing memory allocation is less than 128KB and it is not a dense non-overlapping tensor.

CUTLASS 4.3.3

CuTe DSL

• New features

  • Supported namedtuple and kwargs for JIT function arguments in tvm-ffi
  • Supported variadic tuples for JIT function argument in tvm-ffi

• Bug fixing and improvements

  • Fixed an issue when JIT function argument with union type annotation for tvm-ffi
  • Clearer error message for the case of runtime error cudaErrorInsufficientDriver

CUTLASS 4.3.2

CuTe DSL

• New features

  • New env var CUTE_DSL_CACHE_DIR to specify the path for dumping caches

• Bug fixing and improvements

  • Fixed an issue of CUDA JitExecutor when unloading kernels
  • Fixed an issue of allocating max smem when there's statically allocated smem

CUTLASS 4.3.1

CuTe DSL

• New features
  • Added Blackwell SM103 support
  • Multiple dependent DSOs in the wheel have been merged into one single DSO
• Bug fixing and improvements
  • Fixed device reset issue with tvm-ffi
  • Fixed tvm-ffi export compiled function

CUTLASS C++

• Support blockscaled variant of ragged contiguous grouped gemm with the new simplified MoE API in example 92.
  • The new example works for all microscaling types.

CUTLASS 4.3.0

CuTe DSL

• New features:
  • Supported Apache TVM-FFI for further reduced host runtime overhead for JIT functions, better PyTorch and ML frameworks interopability
  • Added fake tensor and stream to decouple compile jit function with "from_dlpack" flow. Now we no longer require users to have real tensor when compile jit function.
  • Added FastDivmodDivisor with Python operator overloads, new APIs, Cute dialect integration, and optimized static tile scheduler performance for faster index mapping.
  • Added l2 cache evict priority for tma related ops. Users could do fine-grain l2 cache control.
• Debuggability improvements:
  • Supported source location tracking for DSL APIs (Allow tools like nsight profiling to correlate perf metrics with Python source code)
  • Supported dumping PTX and CUBIN code: Hello World Example
• More examples and notebooks to get started with CuTe DSL:
  • Improved performance of elementwise example:
    • Generalize code to handle list of input tensors
    • Generalize TV layout computation to handle different data types
  • Improved Blackwell SM100 persistent dense GEMM with static scheduling:
    • To demonstrate usage of new Pipeline APIs PipelineProducer and PipelineConsumer to simplify code without explicit pipeline state management (Exiting APIs are still maintained)
    • Separated epilogue code for non-TMA and TMA implementation
  • Tutorial for Blackwell GEMM: Basic Blackwell SM100 GEMM
    • Baseline Blackwell GEMM achieves 84% SOL performance with MNK 8K
    • More examples are coming for demo of optimization: Baseline + X
  • Tutorial for Async Pipeline API
  • Reworked elementwise add notebook with more details and detailed explanation about TV layout
    • Updated implementation to handle general data type and multiple inputs
    • Updated explanation for TV layout in simpler language
    • Added visualization of TV Layout with 3rd party utils
  • Benchmark and autotune demonstration
• More examples of authorizing peak-performance kernels:
  • Blackwell SM100 mixed-input GEMM
  • Blackwell SM100 persistent blockwise dense GEMM
  • Blackwell SM100 persistent blockwise contiguous grouped dense GEMM
  • Blackwell SM100 persistent blockwise masked grouped dense GEMM
  • Blackwell SM100 fmha bwd
  • Blackwell SM100 mla
  • Hopper SM90 persistent dense GEMM with static scheduling
  • Blackwell GeForce batched dense GEMM
  • Ampere HSTU Attention
• API updates:
  • Please refer to DSL API changelog for details
• Bug fixings and improvements
  • Add mma_tiler_n=64 and mma_tiler_n=192 support in Blackwell SM100 persistent dense blockscaled GEMM with static scheduling.
  • Fixed TensorSSA.reduce to support static value as initial value
  • Updated docstring for following APIs to be more concise and easier to understand:
    • make_layout_tv
    • is_static
    • PipelineAsync
    • SmemAllocator
  • Fixed documentation for pipeline, utils and cute.math
  • Added overlapping accumulator optimization for block tile N = 256 case for better epilogue latency hiding in Blackwell SM100 persistent dense blockscaled GEMM with static scheduling.
  • Fixed TensorSSA.getitem indexing to match CuTe's indexing convention
  • Fixed an issue with cutlass.max and cutlass.min
  • Fixed an issue with mark_compact_shape_dynamic

CUTLASS C++

• Further enhance Blackwell SM100 Attention kernels in example 77.
  • Add softmax skip correction.
  • Fix a shared memory allocation bug where it needs to opt in maximum dynamics shared memory explicitly once it exceeds 48KB.
  • Fix a dead hang issue caused by early return warp.
• Add support through cmdline argument lists for batch, no_verif, cluster_shape and cluster_shape_fallback in example 89.
• Add Ragged Contiguous Grouped gemm kernel in example 92.
  • This kernel uses a TMA 3D load to load the weights matrix and use the tensormap update method to load activations.
• Add 256x128 tile size support for Hopper SM90 deepgemm in example 67.
  • Performance is optimized to align with Deepseek implementation.
• Simplification of API for MoE gemms.
  • Instead of requiring users to call several cute utilities to set up the stride, API moe_stride_utils is introduced to help setup strides in the kernel.
  • Instead of requiring users to set vectors like problem_shapes_device and problem_shapes_hosts, a new problem shape struct called MoEProblemShape is introduced which takes in max_m, max_n, max_k and counts vector as input and deduce problem shapes internally whenever required.
• Enable GEMM_K = 0 in grouped gemm.
• Optimize group gemm kernels by enabling async TMA desc update.
• Support Blackwell SM100 convolution stream-K kernel.
  • Unit tests: fprop_streamK, dgrad_streamK, wgrad_streamK.
• Add Blackwell SM100 sparse gemm compressor unit tests.
  • Unit tests: compressor_fp16.
  • Add sub-bytes and runtime data type support in compressor unit test testbed.
• Add profiler support for:
  • Blackwell SM100 and SM120 blockscaled sparse kernels.
  • New MoE grouped gemm API.
  • Blackwell SM100 cpasync kernel.
• Fix some kernel issues:
  • Fix a race check issue of Blackwell SM103 kernels by adding missing elect one for prefetch barrier initialization.
  • Allow user to directly specify the number of stages for Hopper sm90 mixed input gemm.
  • Remove warnings caused by cuda vector type alignment setting in CUDA 13.
  • Remove problematic cutlass::int8_t and replace it with int8_t.
  • Fix a few bugs in distributed gemm API and examples.
  • Fix handling negative zero in sparse compressor.
  • Add missing wait_on_dependent_grids for PDL use case.
• Fix some profiler issues:
  • Add some missing reference kernels.
  • Support VoidC reference kernels.
  • Add calculation of scale factor A and B in function bytes_with_problem_shape of block scaled profiler.
  • Fix an issue when epilogue tile N is not divided by default subtile N.
• Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
• Optimal code generation with CUDA toolkit versions 13.0U1.

CUTLASS 4.2.1

CuTe DSL

• Bug fixings and improvements
  • Fixed an issue when running DSL codes with cuda-python 13.0
  • Fixed an issue when running inductor with DSL codes
  • Fixed an issue with unexpected logging when running DSL codes in FlashInfer
  • Fixed the issue reported in #2647
  • Fixed an issue when conditional define of variables outside of dynamic control flow

CUTLASS C++

• Bypass EVT for nosmem blockwise kernels on Blackwell.
• Rename cutlass/python/cutlass directory to cutlass/python/cutlass_cppgen.

CUTLASS 4.2.0

CuTe DSL

• More Python versions are now supported for both x86-64 and aarch64, including
  • Python 3.10, 3.11, 3.12, and 3.13
• Added new example and updated notebook to get started with CuTe DSL
  • Call kernels with dlpack bypassed
  • Updates on TensorSSA demonstration
    • Added a section for introducing the broadcast
• API updates
  • Please refer to DSL API changelog for details
• Bug fixings and improvements
  • Fixed cute.print_tensor for coordinate tensor
  • Fixed cute.print for tuple of layouts
  • Fixed frozen object is not properly updated after fully assigned in dynamic control flow
  • Fixed assign tuple/list element in a dynamic control flow may cause compilation failure
  • Improved error message when CUDA context is not initialized
  • Improved docstring of congruent and weakly_congruent

CUTLASS C++

• Support for Blackwell SM103 kernels for B300 GPUs.
  • Collective mainloop codes: Blockscaled datatypes with support for dense GEMM mainloop
  • New GEMM and epilogue dispatch policies for collectives, kernel layers, and builders.
  • Kernel codes: Blockscaled datatypes with support for dense GEMM kernel.
• Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM103 architecture:
  • Blockscaled ultra fp4 dense GEMM.
  • Blockscaled ultra fp4 dense grouped GEMM.
• Set of unit tests that demonstrate the usage of Blackwell SM103 blockscaled GEMM
  • Unit test files with prefix name of sm103_ under GEMM device unit tests.
• Support for Blackwell SM121 kernels for DGX Spark GPUs.
  • Share the major codes with Blackwell SM120 kernels.
• Add support for heuristics-based kernel filtering and autotuning using nvidia-matmul-heuristics to find the best kernels for a given scenario.
  • Details please refer to heuristics doc.
• Further enhance Blackwell SM100 Attention kernels in example 77.
  • Add fused reduction kernel support for cutlass MLA.
  • Add softmax skip correction.
  • Support for GQA in FMHA backward kernel.
  • Fix an issue where get_unmasked_trip_count may return a negative value.
  • Fix an issue where mbarriers are initialized with a zero arrival count.
  • Fix a corner case issue where the sequence length of q is not a multiple of tile_q.
  • Remove tma padding for forward kernel inputs.
• Add Blackwell SM100 kernels for MoEs (focusing on Low-Latency inference performance): example 92. It uses TMA (for weights) and CPASYNC (for tokens) to load input matrices and allow only one problem dimension to vary across groups/experts, unlike general Grouped GEMMs. Note: further API simplifications and kernel improvements are upcoming. Any feedback on API is welcome.
• Further enhance blockwise and groupwise GEMMs on Hopper and Blackwell
  • On Blackwell SM120, a blockwise gemm kernel is added: example 87.
  • On Hopper, add K major scale factor support for SM90 blockwise kernels.
  • On Hopper, relax the restriction that the k dimension of the problem size has to be the multiple of the k dimension of the tile size.
  • On Hopper, grouped version supports the case when k = 0.
• Support for Blackwell SM100 fp4 gemv kernels.
  • Kernel codes: Gemv kernel.
  • Example codes: example 91
• Support for Blackwell SM100 legacy mixed input GEMM kernels.
  • Collective mainloop codes: Mixed input mainloop.
  • Kernel codes: Mixed input kernel.
  • Example codes: example 86.
• Support for Blackwell SM100 cpasync kernel.
  • Collective mainloop codes: cpasync mainloop.
  • Kernel codes: cpasync kernel.
• Support Blackwell SM120 mixed input blockscaled grouped GEMM.
• Instantiating more Blackwell kernels in profiler.
  • Blackwell SM100 and SM103 kernels support CUTLASS_LIBRARY_INSTANTIATION_LEVEL to instantiate all possible combinations.
  • To use this feature, CUTLASS_LIBRARY_KERNELS must be non-empty. Profiler will combine CUTLASS_LIBRARY_KERNELS and CUTLASS_LIBRARY_INSTANTIATION_LEVEL to instantiate specific kernels.
  • Details please check Profiler Doc.
• Fix some profiler issues:
  • Modify default cluster callback values to none 0 to avoid profiler failure when these values are not set in command line.
  • Fix some no output and timeout issues.
  • Fix Pingpong Blockwise Hopper library generation.
• From CUDA 13.0, the Blackwell SM101 for Thor GPUs is renamed to SM110.
  • For CUDA toolkit version < 13.0, SM101 is still used for Thor GPUs.
  • For CUDA toolkit version >= 13.0, SM110 is used for Thor GPUs and SM101 is no longer valid.
• Rename legacy Python API package from cutlass to cutlass_cppgen and add Blackwell EVT support to legacy Python interface.
  • Restructuring the C++ Blackwell SM100 Collective Epilogue Builder to work with the Python interface's EpilogueDescriptors.
  • Added Blackwell SM100 EVT Emitter on the Python side and routed most emission through Hopper SM90 Emitter.
  • Added some support for running SM100 kernels via the Python interface.
• CuTe changes:
  • Fix inaccurate GridDim calculation under CuTe tutorial.
  • Add movmatrix support.
  • Fix smallest MMA-N allowed for Blackwell fp8 and fp16 gemm kernels.
  • Support fp16 accmulator for sm89 fp8 mma.
  • Shorten nullspace implementation.
  • Isolate and comment on cosize hacks.
  • Important documentation correction: E<0,1> == 1@0@1.
• Fix some kernel issues:
  • Fix Hopper SM90 group gemm kernel to only use the commit group and wait group instead of also waiting on mbarriers.
  • Fix a tiny bug when K is large for Blackwell SM103 fp4 grouped GEMM kernel.
• Add following unit tests:
  • fp16 accmulator for sm89 fp8 mma
  • movmatrix test
  • fp8 narrow mma n and fp16 narrow mma n
• Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
• Optimal code generation with CUDA toolkit versions 13.0U1.

CUTLASS 4.1.0

CuTe DSL

• Add aarch64 support, you can now pip install nvidia-cutlass-dsl on GB200 systems!
• More examples demonstrating how to use CuTe DSL to write peak-performance kernels
  • Blackwell Mamba2 SSD
  • Blackwell SM100 persistent dense blockscaled GEMM with static scheduling
• API updates
  • Please refer to FUNCTIONALITY.md for details

CUTLASS C++

• Further enhance Blackwell SM100 Attention kernels in example 77.
  • Add variable sequence length support for FMHA Backward kernel.
  • Add varlen test support to Backward runner.
  • Codes support empty batch sequences.
• Replace subbyte_iterator with cute::recast_ptr when constructing logical iterators/arrays.
• CuTe changes:
  • Rewrite ArithTuple and ScaledBasis for robustness and clarity.
  • Remove buggy and kludgy get_layoutA|B|C_MN and friends from Atoms/TiledX.
  • Factor out print_latex and friends and rewrite.
  • Factor out print_svg and friends and rewrite.
• Support Blackwell SM100 SIMT packed fp32x2 kernels.
• Support residual add for implicit gemm kernels.
• Various fixes for CUTLASS C++ Python interface's EVT tracer:
  • Add verifier for sm90 to report the invalid input.
  • When adding an edge to the graph, if the edge already exists, add an identity compute node to avoid having multiple parallel edges.
  • Register operations of tanh, sigmoid, exp, gelu to the python ast frontend.
  • Replace the NotImplemented Error by packing all nodes into a single topological visitor node as a fallback.
• Fix profiler bugs in exhaustive perf search.
  • Fix incorrect cluster shape output issue when doing exhaustive search.
  • Fix a bug in profiler grouped GEMM for setting tile scheduler swizzles, cluster shapes, and raster orders.
• Fix some profiler issues.
  • Complete the reference for Blackwell blockwise gemm kernels.
  • Fix incorrect regex logic for L1 test.

CUTLASS 4.0.0

CuTe DSL

CuTe DSL is a Python DSL centered around CuTe's abstractions

• Enables authoring kernels in Python to reach peak performance on NVIDIA GPUs
• Core DSL implementation files
• DSL quick start
• DSL Overview
• Educational notebooks for getting started with CuTe DSL

CUTLASS C++

• Support Family Specific Architecture Features which was introduced in CUDA 12.9
• Further improved Blockwise and Groupwise GEMMs on Hopper and Blackwell
• Enhance Blackwell SM100 Attention kernels in example 77
• Add Blackwell SM100 implicit GEMM conv fprop/dgrad/wgrad unit tests
• New Hopper SM90 FMHA example, similar in design to the existing Blackwell FMHA
• Cute enhancements: CuTe C++ reduce op
• Other functional and performance enhancements