diff --git a/qwen3_6_scripts/probe_ixinfer.sh b/qwen3_6_scripts/probe_ixinfer.sh
new file mode 100755
index 00000000..195f40e4
--- /dev/null
+++ b/qwen3_6_scripts/probe_ixinfer.sh
@@ -0,0 +1,98 @@
+#!/bin/bash
+# probe_ixinfer.sh — cat the ixinfer.h header and check cublas_v2.h for batched GEMM signatures
+echo "=== 1. ixinfer.h full content ==="
+cat /usr/local/corex/include/ixinfer.h
+echo ""
+echo "=== 2. cublas_v2.h batched GEMM declarations ==="
+grep -B2 -A15 "StridedBatched\|GemmBatched" /usr/local/corex/include/cublas_v2.h 2>/dev/null
+echo ""
+echo "=== 3. cublasLt.h — MatmulDescCreate signature ==="
+grep -B2 -A10 "cublasLtMatmulDescCreate\|cublasComputeType_t\|CUBLAS_COMPUTE" /usr/local/corex/include/cublasLt.h 2>/dev/null | head -40
+echo ""
+echo "=== 4. Quick batched GEMM functional test ==="
+cat > /tmp/test_batched_gemm.cu << 'CUDA'
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+#include <cstdio>
+#include <cstdlib>
+
+int main() {
+    cublasHandle_t handle;
+    cublasCreate(&handle);
+    cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH);
+
+    // Simulate: 8 experts, each doing (1, 128) x (128, 256) = (1, 256)
+    // Strided batch: A is (8, 1, 128), B is (8, 128, 256), C is (8, 1, 256)
+    int batch = 8;
+    int m = 1, n = 256, k = 128;
+
+    __half *d_A, *d_B, *d_C;
+    cudaMalloc(&d_A, batch * m * k * sizeof(__half));
+    cudaMalloc(&d_B, batch * k * n * sizeof(__half));
+    cudaMalloc(&d_C, batch * m * n * sizeof(__half));
+
+    // Fill with ones for testing
+    __half one_h = __float2half(1.0f);
+    // (skip fill for speed, just test the API call)
+
+    __half alpha_h = __float2half(1.0f);
+    __half beta_h = __float2half(0.0f);
+
+    // cublasHgemmStridedBatched:
+    // C[i] = alpha * A[i] * B[i] + beta * C[i]
+    // A[i]: m x k, B[i]: k x n, C[i]: m x n
+    cublasStatus_t st = cublasHgemmStridedBatched(
+        handle,
+        CUBLAS_OP_N,    // transa
+        CUBLAS_OP_N,    // transb
+        n,              // m (cublas is column-major, so swap m/n for row-major)
+        m,              // n
+        k,              // k
+        &alpha_h,
+        d_B, n, (long long)(k * n),   // B, ldb, strideB
+        d_A, k, (long long)(m * k),   // A, lda, strideA
+        &beta_h,
+        d_C, n, (long long)(m * n),   // C, ldc, strideC
+        batch
+    );
+
+    cudaDeviceSynchronize();
+    printf("cublasHgemmStridedBatched status: %d (0=SUCCESS)\n", st);
+
+    // Also test GemmStridedBatchedEx for fp16 compute
+    float alpha_f = 1.0f, beta_f = 0.0f;
+    st = cublasGemmStridedBatchedEx(
+        handle,
+        CUBLAS_OP_N, CUBLAS_OP_N,
+        n, m, k,
+        &alpha_f,
+        d_B, CUDA_R_16F, n, (long long)(k * n),
+        d_A, CUDA_R_16F, k, (long long)(m * k),
+        &beta_f,
+        d_C, CUDA_R_16F, n, (long long)(m * n),
+        batch,
+        CUDA_R_32F,     // computeType
+        CUBLAS_GEMM_DEFAULT_TENSOR_OP
+    );
+    cudaDeviceSynchronize();
+    printf("cublasGemmStridedBatchedEx(fp16in_fp32compute) status: %d\n", st);
+
+    cudaFree(d_A);
+    cudaFree(d_B);
+    cudaFree(d_C);
+    cublasDestroy(handle);
+    printf("Batched GEMM test done.\n");
+    return 0;
+}
+CUDA
+
+/usr/local/corex/bin/clang++ --cuda-gpu-arch=ivcore10 --cuda-path=/usr/local/corex \
+    -I/usr/local/corex/include -L/usr/local/corex/lib64 -lcudart -lcublas \
+    /tmp/test_batched_gemm.cu -o /tmp/test_batched_gemm 2>&1
+
+if [ -f /tmp/test_batched_gemm ]; then
+    echo "Compile: SUCCESS"
+    /tmp/test_batched_gemm
+else
+    echo "Compile: FAILED"
+fi