diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 08428ea3f..14b1ecf7d 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -439,6 +439,7 @@ static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullpt
 struct ggml_tensor_extra_gpu {
     void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
     cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs
+    bool copied;
 };
 
 // this is faster on Windows
@@ -4355,8 +4356,9 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
 }
 
 // rope == RoPE == rotary positional embedding
-static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p0,
-                                const float p_delta, const int p_delta_rows, const float theta_scale) {
+
+static __global__ void rope_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                                const int p_delta_rows, const float theta_scale) {
     const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (col >= ncols) {
@@ -4365,8 +4367,11 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
 
     const int row = blockDim.x*blockIdx.x + threadIdx.x;
     const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
 
-    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
+    const int p = pos != nullptr ? pos[i2] : 0;
+    const float p0 = p * freq_scale;
+    const float theta = p0*powf(theta_scale, col/2);
     const float sin_theta = sinf(theta);
     const float cos_theta = cosf(theta);
 
@@ -4377,8 +4382,8 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
     dst[i + 1] = x0*sin_theta + x1*cos_theta;
 }
 
-static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
-                                const float p_delta, const int p_delta_rows, const float theta_scale) {
+static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                                     const int p_delta_rows, const float theta_scale) {
     const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (col >= ncols) {
@@ -4387,8 +4392,11 @@ static __global__ void rope_neox_f32(const float * x, float * dst, const int nco
 
     const int row = blockDim.x*blockIdx.x + threadIdx.x;
     const int i = row*ncols + col/2;
+    const int i2 = row/p_delta_rows;
 
-    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
+    const int p = pos != nullptr ? pos[i2] : 0;
+    const float p0 = p * freq_scale;
+    const float theta = p0*powf(theta_scale, col/2);
     const float sin_theta = sinf(theta);
     const float cos_theta = cosf(theta);
 
@@ -4399,8 +4407,8 @@ static __global__ void rope_neox_f32(const float * x, float * dst, const int nco
     dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
 }
 
-static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p0,
-                                    const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx) {
+static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
+                                    const int p_delta_rows, const float theta_scale, const int n_ctx) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
     const int half_n_dims = ncols/4;
 
@@ -4410,11 +4418,13 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
 
     const int row = blockDim.y*blockIdx.y + threadIdx.y;
     const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
 
     const float col_theta_scale = powf(theta_scale, col);
-    const float p = p0 + p_delta*(row/p_delta_rows);
+     // FIXME: this is likely wrong
+    const int p = pos != nullptr ? pos[i2] : 0;
 
-    const float theta = min(p, p_delta*(n_ctx - 2))*col_theta_scale;
+    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
     const float sin_theta = sinf(theta);
     const float cos_theta = cosf(theta);
 
@@ -4424,7 +4434,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
     dst[i + 0]           = x0*cos_theta - x1*sin_theta;
     dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
 
-    const float block_theta = max(p - p_delta*(n_ctx - 2), 0.f)*col_theta_scale;
+    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
     const float sin_block_theta = sinf(block_theta);
     const float cos_block_theta = cosf(block_theta);
 
@@ -5361,31 +5371,31 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
     scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
 }
 
-static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
-                          const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
     GGML_ASSERT(ncols % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
-    rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
+    rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
 }
 
-static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
-                          const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
     GGML_ASSERT(ncols % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
-    rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
+    rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
 }
 
-static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
-                              const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
+static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
+                              const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
     GGML_ASSERT(ncols % 4 == 0);
     const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
     const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
     const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale, n_ctx);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx);
 }
 
 static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
@@ -6069,9 +6079,10 @@ inline void ggml_cuda_op_rope(
 
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
+    const int64_t ne2 = dst->ne[2];
     const int64_t nrows = ggml_nrows(src0);
 
-    const int n_past = ((int32_t *) dst->op_params)[0];
+    //const int n_past = ((int32_t *) dst->op_params)[0];
     const int n_dims = ((int32_t *) dst->op_params)[1];
     const int mode   = ((int32_t *) dst->op_params)[2];
     const int n_ctx  = ((int32_t *) dst->op_params)[3];
@@ -6082,19 +6093,37 @@ inline void ggml_cuda_op_rope(
     memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
+    // const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(src1->ne[0] == ne2);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    int * pos = nullptr;
+    if ((mode & 1) == 0) {
+        struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+        pos = (int *) src1_extra->data_device[id];
+        if (!src1_extra->copied) {
+            CUDA_CHECK(cudaMemcpyAsync(pos, src1->data, ggml_nbytes(src1), cudaMemcpyHostToDevice, main_stream));
+            src1_extra->copied = true;
+        }
+    }
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
 
     // compute
     if (is_glm) {
-        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, n_ctx, main_stream);
+        GGML_ASSERT(false);
+        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream);
     } else if (is_neox) {
         GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
-        rope_neox_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, main_stream);
+        rope_neox_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
     } else {
-        rope_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, main_stream);
+        rope_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
     }
 
     (void) src1;
diff --git a/llama.cpp b/llama.cpp
index 5f40a9b5f..df0b39bfb 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2708,6 +2708,7 @@ static struct ggml_cgraph * llm_build_llama(
 
     // KQ_pos - contains the positions
     struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
     ggml_allocr_alloc(lctx.alloc, KQ_pos);
     if (!ggml_allocr_is_measure(lctx.alloc)) {
         int * data = (int *) KQ_pos->data;
@@ -2719,6 +2720,7 @@ static struct ggml_cgraph * llm_build_llama(
     // shift the entire K-cache if needed
     if (do_rope_shift) {
         struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
         ggml_allocr_alloc(lctx.alloc, K_shift);
         if (!ggml_allocr_is_measure(lctx.alloc)) {
             int * data = (int *) K_shift->data;
@@ -3092,6 +3094,7 @@ static struct ggml_cgraph * llm_build_baichaun(
 
     // KQ_pos - contains the positions
     struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
     ggml_allocr_alloc(lctx.alloc, KQ_pos);
     if (!ggml_allocr_is_measure(lctx.alloc)) {
         int * data = (int *) KQ_pos->data;
@@ -3103,6 +3106,7 @@ static struct ggml_cgraph * llm_build_baichaun(
     // shift the entire K-cache if needed
     if (do_rope_shift) {
         struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
         ggml_allocr_alloc(lctx.alloc, K_shift);
         if (!ggml_allocr_is_measure(lctx.alloc)) {
             int * data = (int *) K_shift->data;
@@ -3496,6 +3500,7 @@ static struct ggml_cgraph * llm_build_falcon(
 
     // KQ_pos - contains the positions
     struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    offload_func_kq(KQ_pos);
     ggml_allocr_alloc(lctx.alloc, KQ_pos);
     if (!ggml_allocr_is_measure(lctx.alloc)) {
         int * data = (int *) KQ_pos->data;
@@ -3507,6 +3512,7 @@ static struct ggml_cgraph * llm_build_falcon(
     // shift the entire K-cache if needed
     if (do_rope_shift) {
         struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        offload_func_kq(K_shift);
         ggml_allocr_alloc(lctx.alloc, K_shift);
         if (!ggml_allocr_is_measure(lctx.alloc)) {
             int * data = (int *) K_shift->data;