diff --git a/ggml/src/ggml-cuda/ssm_scan.cu b/ggml/src/ggml-cuda/ssm_scan.cu
index fab0814b2..f95b34629 100644
--- a/ggml/src/ggml-cuda/ssm_scan.cu
+++ b/ggml/src/ggml-cuda/ssm_scan.cu
@@ -175,146 +175,3 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context &ctx, ggml_tensor *dst) {
                     src3->nb[1], src4->nb[1], src4->nb[2], src5->nb[1],
                     src5->nb[2], dst_d, nc, nr, n_t, n_s, stream);
 }
-
-// #include "ssm_scan.cuh"
-
-// template <int block_size>
-// static __global__ void ssm_scan_f32(
-//     const float *__restrict__ src0, const float *__restrict__ src1,
-//     const float *__restrict__ src2, const float *__restrict__ src3,
-//     const float *__restrict__ src4, const float *__restrict__ src5,
-//     const int src0_nb1, const int src0_nb2, const int src1_nb0,
-//     const int src1_nb1, const int src1_nb2, const int src1_nb3,
-//     const int src2_nb0, const int src2_nb1, const int src2_nb2,
-//     const int src3_nb1, const int src4_nb1, const int src4_nb2,
-//     const int src5_nb1, const int src5_nb2, float *__restrict__ dst,
-//     const int nc, const int nr, const int n_t, const int n_s) {
-//   //    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-//   const int tid = threadIdx.x;
-//   const int i3 = threadIdx.y;
-
-//   const int ith = tid;
-//   const int nth = WARP_SIZE;
-
-//   // rows per thread
-//   const int dr = (nr + nth - 1) / nth;
-
-//   // row range for this thread
-//   const int ir0 = dr * ith;
-//   const int ir1 = min(ir0 + dr, nr);
-//   const int ir = ir1 - ir0;
-//   for (int i2 = 0; i2 < n_t; ++i2) {
-//     const float *s0 =
-//         (const float *)((const char *)src0 + ir0 * src0_nb1 +
-//                         i3 * src0_nb2);  // {d_state, d_inner, n_s}
-//     const float *x =
-//         (const float *)((const char *)src1 + ir0 * src1_nb0 + i2 * src1_nb1 +
-//                         i3 * src1_nb2);  // {d_inner, n_t, n_s}
-//     const float *dt =
-//         (const float *)((const char *)src2 + ir0 * src2_nb0 + i2 * src2_nb1 +
-//                         i3 * src2_nb2);  // {d_inner, n_t, n_s}
-//     const float *A = (const float *)((const char *)src3 +
-//                                      ir0 * src3_nb1);  // {d_state, d_inner}
-//     const float *B = (const float *)((const char *)src4 + i2 * src4_nb1 +
-//                                      i3 * src4_nb2);  // {d_state, n_t, n_s}
-//     const float *C = (const float *)((const char *)src5 + i2 * src5_nb1 +
-//                                      i3 * src5_nb2);  // {d_state, n_t, n_s}
-//     float *y = (float *)((char *)dst + ir0 * src1_nb0 + i2 * src1_nb1 +
-//                          i3 * src1_nb2);  // {d_inner, n_t, n_s}
-//     float *s = (float *)((char *)dst + ir0 * src0_nb1 + i3 * src0_nb2 +
-//                          src1_nb3);  // {d_state, d_inner, n_s}
-
-//     // use the output as the source for the next token-wise iterations
-//     if (i2 > 0) {
-//       s0 = s;
-//     }
-
-//     // d_inner
-//     for (int i1 = 0; i1 < ir; ++i1) {
-//       // ref:
-//       //
-//       https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
-//       float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
-//       float x_dt = x[i1] * dt_soft_plus;
-//       float sumf = 0.0f;
-// // d_state
-// #pragma unroll
-//       for (int i0 = 0; i0 < nc; ++i0) {
-//         int i = i0 + i1 * nc;
-//         // state = prev_state * dA + dB * x
-//         float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
-//         // y = rowwise_dotprod(state, C)
-//         sumf += state * C[i0];
-//         s[i] = state;
-//       }
-//       y[i1] = sumf;
-//     }
-//   }
-// }
-
-// static void ssm_scan_f32_cuda(
-//     const float *src0, const float *src1, const float *src2, const float
-//     *src3, const float *src4, const float *src5, const int src0_nb1, const
-//     int src0_nb2, const int src1_nb0, const int src1_nb1, const int src1_nb2,
-//     const int src1_nb3, const int src2_nb0, const int src2_nb1, const int
-//     src2_nb2, const int src3_nb1, const int src4_nb1, const int src4_nb2,
-//     const int src5_nb1, const int src5_nb2, float *dst, const int nc, const
-//     int nr, const int n_t, const int n_s, cudaStream_t stream) {
-//   const dim3 block_dims(WARP_SIZE, n_s, 1);
-//   const int nblocks = 1;  // TODO
-
-//   ssm_scan_f32<WARP_SIZE><<<nblocks, block_dims, 0, stream>>>(
-//       src0, src1, src2, src3, src4, src5, src0_nb1, src0_nb2, src1_nb0,
-//       src1_nb1, src1_nb2, src1_nb3, src2_nb0, src2_nb1, src2_nb2, src3_nb1,
-//       src4_nb1, src4_nb2, src5_nb1, src5_nb2, dst, nc, nr, n_t, n_s);
-// }
-
-// void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context &ctx, ggml_tensor *dst)
-// {
-//   const struct ggml_tensor *src0 = dst->src[0];  // s
-//   const struct ggml_tensor *src1 = dst->src[1];  // x
-//   const struct ggml_tensor *src2 = dst->src[2];  // dt
-//   const struct ggml_tensor *src3 = dst->src[3];  // A
-//   const struct ggml_tensor *src4 = dst->src[4];  // B
-//   const struct ggml_tensor *src5 = dst->src[5];  // C
-
-//   const int64_t nc = src0->ne[0];   // d_state
-//   const int64_t nr = src0->ne[1];   // d_inner
-//   const int64_t n_t = src1->ne[1];  // number of tokens per sequence
-//   const int64_t n_s = src0->ne[2];  // number of sequences in the batch
-
-//   GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) ==
-//               ggml_nelements(dst));
-//   GGML_ASSERT(src0->nb[0] == sizeof(float));
-//   GGML_ASSERT(src1->nb[0] == sizeof(float));
-//   GGML_ASSERT(src2->nb[0] == sizeof(float));
-//   GGML_ASSERT(src3->nb[0] == sizeof(float));
-//   GGML_ASSERT(src4->nb[0] == sizeof(float));
-//   GGML_ASSERT(src5->nb[0] == sizeof(float));
-//   // required for the dot product between s and C
-//   GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
-//   // required for per-sequence offsets for states
-//   GGML_ASSERT(src0->nb[2] == src0->ne[0] * src0->ne[1] * sizeof(float));
-//   // required to get correct offset for state destination (i.e. src1->nb[3])
-//   GGML_ASSERT(src1->nb[3] ==
-//               src1->ne[0] * src1->ne[1] * src1->ne[2] * sizeof(float));
-
-//   const float *src0_d = (const float *)src0->data;
-//   const float *src1_d = (const float *)src1->data;
-//   const float *src2_d = (const float *)src2->data;
-//   const float *src3_d = (const float *)src3->data;
-//   const float *src4_d = (const float *)src4->data;
-//   const float *src5_d = (const float *)src5->data;
-//   float *dst_d = (float *)dst->data;
-//   cudaStream_t stream = ctx.stream();
-
-//   GGML_ASSERT(src0->type == GGML_TYPE_F32);
-//   GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-//   ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d,
-//   src0->nb[1],
-//                     src0->nb[2], src1->nb[0], src1->nb[1], src1->nb[2],
-//                     src1->nb[3], src2->nb[0], src2->nb[1], src2->nb[2],
-//                     src3->nb[1], src4->nb[1], src4->nb[2], src5->nb[1],
-//                     src5->nb[2], dst_d, nc, nr, n_t, n_s, stream);
-// }
\ No newline at end of file