v4.3.3 update. (#2868)

README.md

@@ -1,9 +1,9 @@
 ![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
 # Overview
 
-# CUTLASS 4.3.2
+# CUTLASS 4.3.3
 
-_CUTLASS 4.3.2 - Dec 2025_
+_CUTLASS 4.3.3 - Dec 2025_
 
 CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM)
 and related computations at all levels and scales within CUDA. It incorporates strategies for
@@ -54,6 +54,8 @@ To get started quickly - please refer :
   - Added Blackwell SM103 support.
   - Multiple dependent DSOs in the wheel have been merged into one single DSO.
   - New env var `CUTE_DSL_CACHE_DIR` to specify the path for dumping caches.
+  - Supported namedtuple and kwargs for JIT function arguments in tvm-ffi.
+  - Supported variadic tuples for JIT function argument in tvm-ffi.
 * 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](https://github.com/NVIDIA/cutlass/blob/main/examples/python/CuTeDSL/notebooks/hello_world.ipynb)
@@ -102,6 +104,8 @@ To get started quickly - please refer :
     - Fixed tvm-ffi export compiled function
     - Fixed an issue of CUDA JitExecutor when unloading kernels
     - Fixed an issue of allocating max smem when there's statically allocated smem
+    - 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 C++
 * Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).