Modify the ck_tile gemm config

Author: ThomasNing

example/ck_tile/17_grouped_gemm/README.md

@@ -4,7 +4,7 @@ The `Grouped GEMM` operators are versions of GEMM that run multiple GEMM operati
 
 ### Preshuffle and Persistence
 
-The grouped GEMM examples include two advanced optimization features:
+The grouped GEMM examples include the following advanced optimization features:
 
 #### Weight Preshuffle
 Weight preshuffle is an optimization technique that reorganizes the B matrix (weights) in memory to improve data access patterns and reduce memory bandwidth requirements. This is particularly beneficial for inference workloads where the same weights are reused across multiple batches.
@@ -26,7 +26,7 @@ Multi-D operations extend the standard GEMM operation by supporting additional e
 - **Implementation**: Available in `grouped_gemm_multi_d.cpp`
 - **Operation**: E = C × D₀ × D₁ (where C = A × B is the standard GEMM result)
 - **Configuration**: Uses `GemmConfigV3`, `GemmConfigV4`, `GemmConfigMemory` template configuration with 2 D tensors
-- **Data Types**: Supports fp16 
+- **Data Types**: Supports fp16, fp8
 - **Benefits**: Enables complex operations like scaling, activation functions, or other elementwise transformations in a single kernel call
 - **Build Target**: `make tile_example_grouped_gemm_multi_d -j`
 

example/ck_tile/18_flatmm/README.md

@@ -18,15 +18,6 @@ $$
 
 - **FLATMM**: An alternative solution as the Preshuffled GEMM in /03_gemm
 
----
-
-## Tile Programming Model
-
-- **Tiles**: Each thread block processes a tile of $C$ for a given batch.
-- **Pipeline**: Modular, supports different memory/computation pipelines and flat/padded layouts.
-
----
-
 
 ---