From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Jesse Gross <jesse@ollama.com>
Date: Wed, 23 Jul 2025 11:58:49 -0700
Subject: [PATCH] ggml: No-alloc mode

Callers can set a scheduler to be no-alloc, meaning that
it does not allocate memory for tensors or operations. This can
be used for calculating memory requirements. Tensors and graphs
must be recreated with no-alloc set to false before loading data.
---
 ggml/include/ggml-backend.h     |   1 +
 ggml/src/ggml-backend-impl.h    |  16 +++
 ggml/src/ggml-backend.cpp       |  75 ++++++++++-
 ggml/src/ggml-cuda/common.cuh   |  62 ++++++++-
 ggml/src/ggml-cuda/ggml-cuda.cu | 224 ++++++++++++++++++++++++++------
 5 files changed, 333 insertions(+), 45 deletions(-)

diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 93c95602d..dbbb61d9c 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -305,6 +305,7 @@ extern "C" {
 
     // Initialize a backend scheduler, backends with low index are given priority over backends with high index
     GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new_ext(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload, bool alloc_buffers);
     GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
 
     // Provide a hint on the batch size to optimize processing (uses heuristics if unset)
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
index 0f5b03cef..7bdf9d81f 100644
--- a/ggml/src/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -26,12 +26,17 @@ extern "C" {
         size_t                (*get_alloc_size)(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor);
         // (optional) check if tensor data is in host memory and uses standard ggml tensor layout (defaults to false)
         bool                  (*is_host)       (ggml_backend_buffer_type_t buft);
+
+        // (optional) returns a dummy buffer that is equivalent to one created by alloc_buffer but without actually being backed
+        // by memory
+        ggml_backend_buffer_t (*noalloc_buffer)(ggml_backend_buffer_type_t buft, size_t size);
     };
 
     struct ggml_backend_buffer_type {
         struct ggml_backend_buffer_type_i  iface;
         ggml_backend_dev_t device;
         void * context;
+        bool no_alloc;
     };
 
     //
@@ -63,6 +68,7 @@ extern "C" {
         void * context;
         size_t size;
         enum ggml_backend_buffer_usage usage;
+        bool no_alloc;
     };
 
     GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
@@ -117,6 +123,16 @@ extern "C" {
 
         // (optional) sort/optimize the nodes in the graph
         void                      (*graph_optimize)    (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+
+        // (optional) reserves intermediate buffers needed for the compution
+        // if alloc is true, memory is actually allocated, otherwise the required amount is just returned by buffer_size
+        enum ggml_status          (*graph_reserve)     (ggml_backend_t backend, struct ggml_cgraph * cgraph, bool alloc);
+
+        // (optional) returns the memory needed after calling graph_reserve
+        size_t                    (*buffer_size)       (ggml_backend_t backend);
+
+        // (optional) frees memory from intermediate buffers that was allocated either by graph_compute or graph_reserve
+        void                      (*reset)             (ggml_backend_t backend);
     };
 
     struct ggml_backend {
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 498186a7c..7746e8b92 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -36,11 +36,25 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
 }
 
 ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    GGML_ASSERT(buft);
     if (size == 0) {
         // return a dummy buffer for zero-sized allocations
         return ggml_backend_buffer_init(buft, {}, NULL, 0);
     }
+
+    if (buft->no_alloc) {
+        ggml_backend_buffer_t buf;
+
+        if (buft->iface.noalloc_buffer != NULL) {
+            buf = buft->iface.noalloc_buffer(buft, size);
+        } else {
+            buf = ggml_backend_buffer_init(buft, {}, NULL, size);
+        }
+
+        buf->no_alloc = true;
+        return buf;
+    }
+
+    GGML_ASSERT(buft);
     return buft->iface.alloc_buffer(buft, size);
 }
 
@@ -94,7 +108,8 @@ ggml_backend_buffer_t ggml_backend_buffer_init(
         /* .buft      = */ buft,
         /* .context   = */ context,
         /* .size      = */ size,
-        /* .usage     = */ GGML_BACKEND_BUFFER_USAGE_ANY
+        /* .usage     = */ GGML_BACKEND_BUFFER_USAGE_ANY,
+        /* .no_alloc  = */ false
     };
 
     return buffer;
@@ -126,6 +141,12 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
         return NULL;
     }
 
+    // If we aren't allocating memory, return a placeholder non-NULL pointer
+    // that meets alignment requirements
+    if (buffer->no_alloc) {
+        return (void *)ggml_backend_buffer_get_alignment(buffer);
+    }
+
     // FIXME JG: a multi_buffer has a non-zero size, according to the above comment get_base is not optional,
     //     I don't know whether the above comment is correct
     if (!buffer->iface.get_base) {
@@ -736,6 +757,12 @@ struct ggml_backend_sched {
     int debug_realloc;
     int debug_graph_size;
     int debug_prev_graph_size;
+
+    // allocate buffers on attached ggml_backend_buffer_type_t's and during reservation
+    // if false, dummy buffers are used for faster memory sizing calculations
+    // the scheduler needs to be recreated with allocated buffers before it can be used
+    // for computation
+    bool alloc_buffers;
 };
 
 #define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
@@ -1635,6 +1662,17 @@ ggml_backend_sched_t ggml_backend_sched_new(
         size_t graph_size,
         bool parallel,
         bool op_offload) {
+            return ggml_backend_sched_new_ext(backends, bufts, n_backends, graph_size, parallel, op_offload, true);
+        }
+
+ggml_backend_sched_t ggml_backend_sched_new_ext(
+        ggml_backend_t * backends,
+        ggml_backend_buffer_type_t * bufts,
+        int n_backends,
+        size_t graph_size,
+        bool parallel,
+        bool op_offload,
+        bool alloc_buffers) {
     GGML_ASSERT(n_backends > 0);
     GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
     GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU);
@@ -1687,11 +1725,14 @@ ggml_backend_sched_t ggml_backend_sched_new(
                 sched->events[b][c] = ggml_backend_event_new(backends[b]->device);
             }
         }
+
+        sched->bufts[b]->no_alloc = !alloc_buffers;
     }
 
     sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
     sched->op_offload = op_offload;
     sched->batch_size = -1;
+    sched->alloc_buffers = alloc_buffers;
 
     ggml_backend_sched_reset(sched);
 
@@ -1706,6 +1747,10 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
         for (int c = 0; c < sched->n_copies; c++) {
             ggml_backend_event_free(sched->events[b][c]);
         }
+
+        if (sched->backends[b]->iface.reset != NULL) {
+            sched->backends[b]->iface.reset(sched->backends[b]);
+        }
     }
     ggml_gallocr_free(sched->galloc);
     ggml_free(sched->ctx);
@@ -1765,6 +1810,24 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
         return false;
     }
 
+    if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
+        return false;
+    }
+
+    struct ggml_backend_sched_split * splits = sched->splits;
+    for (int i = 0; i < sched->n_splits; i++) {
+        struct ggml_backend_sched_split * split = &splits[i];
+        int split_backend_id = split->backend_id;
+        ggml_backend_t split_backend = sched->backends[split_backend_id];
+
+        if (split_backend->iface.graph_reserve != NULL) {
+            enum ggml_status ec = split_backend->iface.graph_reserve(split_backend, &split->graph, sched->alloc_buffers);
+            if (ec != GGML_STATUS_SUCCESS) {
+                return false;
+            }
+        }
+    }
+
     ggml_backend_sched_reset(sched);
 
     return true;
@@ -1870,7 +1933,13 @@ size_t ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched,
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
 
-    return ggml_gallocr_get_attempted_buffer_size(sched->galloc, backend_index);
+    size_t size = ggml_gallocr_get_attempted_buffer_size(sched->galloc, backend_index);
+
+    if (backend->iface.buffer_size != NULL) {
+        size += backend->iface.buffer_size(backend);
+    }
+
+    return size;
 }
 
 void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 9fcb2f9fd..e800ee8f6 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -37,6 +37,41 @@
 #include "vendors/cuda.h"
 #endif // defined(GGML_USE_HIP)
 
+extern bool reserving_graph;
+
+// If we are reserving the graph, pointers might be invalid and will fail if cudaMemcpyAsync tries to validate them.
+// However, since we don't actually expect a result, we don't need to actually do the memcpy.
+static cudaError_t cudaMemcpyAsyncReserve ( void* dst, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream = 0 ) {
+    if (!reserving_graph) {
+        return cudaMemcpyAsync(dst, src, count, kind, stream);
+    } else {
+        return cudaSuccess;
+    }
+}
+
+static cudaError_t cudaMemcpy2DAsyncReserve ( void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream = 0 ) {
+    if (!reserving_graph) {
+        return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, kind, stream);
+    } else {
+        return cudaSuccess;
+    }
+}
+
+static cudaError_t cudaMemsetAsyncReserve ( void* devPtr, int value, size_t count, cudaStream_t stream = 0 ) {
+    if (!reserving_graph) {
+        return cudaMemsetAsync(devPtr, value, count, stream);
+    } else {
+        return cudaSuccess;
+    }
+}
+
+#undef cudaMemcpyAsync
+#define cudaMemcpyAsync cudaMemcpyAsyncReserve
+#undef cudaMemcpy2DAsync
+#define cudaMemcpy2DAsync cudaMemcpy2DAsyncReserve
+#undef cudaMemsetAsync
+#define cudaMemsetAsync cudaMemsetAsyncReserve
+
 #define STRINGIZE_IMPL(...) #__VA_ARGS__
 #define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
 
@@ -941,6 +976,9 @@ struct ggml_cuda_pool {
 
     virtual void * alloc(size_t size, size_t * actual_size) = 0;
     virtual void free(void * ptr, size_t size) = 0;
+
+    virtual bool alloc_memory() = 0;
+    virtual size_t alloc_size() = 0;
 };
 
 template<typename T>
@@ -1232,11 +1270,15 @@ struct ggml_backend_cuda_context {
     // pool
     std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS];
 
-    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device, int stream_no);
+    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device, int stream_no, bool alloc);
 
     ggml_cuda_pool & pool(int device) {
         if (pools[device][curr_stream_no] == nullptr) {
-            pools[device][curr_stream_no] = new_pool_for_device(device, curr_stream_no);
+            bool alloc = true;
+            if (pools[device][0] != nullptr) {
+                alloc = pools[device][0]->alloc_memory();
+            }
+            pools[device][curr_stream_no] = new_pool_for_device(device, curr_stream_no, alloc);
         }
         return *pools[device][curr_stream_no];
     }
@@ -1244,6 +1286,22 @@ struct ggml_backend_cuda_context {
     ggml_cuda_pool & pool() {
         return pool(device);
     }
+
+    void pool_set_alloc(bool alloc) {
+        GGML_ASSERT(pools[device][curr_stream_no] == nullptr || pools[device][curr_stream_no]->alloc_memory() == alloc);
+
+        if (pools[device][curr_stream_no] == nullptr) {
+            pools[device][curr_stream_no] = new_pool_for_device(device, curr_stream_no, alloc);
+        }
+    }
+
+    size_t pool_get_alloc_size(int stream_no) {
+        if (pools[device][stream_no] == nullptr) {
+            return 0;
+        }
+
+        return pools[device][stream_no]->alloc_size();
+    }
 };
 
 struct ggml_cuda_mm_fusion_args_host {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 25548629d..eeaae3fe4 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -365,6 +365,8 @@ const ggml_cuda_device_info & ggml_cuda_info() {
 
 // #define DEBUG_CUDA_MALLOC
 
+#define CUDA_ALIGNMENT 128
+
 // buffer pool for cuda (legacy)
 struct ggml_cuda_pool_leg : public ggml_cuda_pool {
     static const int MAX_BUFFERS = 256;
@@ -377,9 +379,12 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
 
     ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {};
     size_t pool_size = 0;
+    bool allocate = true;
+    size_t last_alloc = 0;
 
-    explicit ggml_cuda_pool_leg(int device) :
-        device(device) {
+    explicit ggml_cuda_pool_leg(int device, bool alloc) :
+        device(device),
+        allocate(alloc) {
     }
 
     ~ggml_cuda_pool_leg() {
@@ -387,7 +392,9 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
         for (int i = 0; i < MAX_BUFFERS; ++i) {
             ggml_cuda_buffer & b = buffer_pool[i];
             if (b.ptr != nullptr) {
-                CUDA_CHECK(cudaFree(b.ptr));
+                if (allocate) {
+                    CUDA_CHECK(cudaFree(b.ptr));
+                }
                 pool_size -= b.size;
             }
         }
@@ -435,8 +442,15 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
         void * ptr;
         size_t look_ahead_size = (size_t) (1.05 * size);
         look_ahead_size = 256 * ((look_ahead_size + 255)/256);
-        ggml_cuda_set_device(device);
-        CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device));
+        if (allocate) {
+            ggml_cuda_set_device(device);
+            if (ggml_cuda_device_malloc(&ptr, look_ahead_size, device) != cudaSuccess) {
+                    last_alloc = look_ahead_size;
+                    throw std::bad_alloc();
+            }
+        } else {
+            ptr = (void *)CUDA_ALIGNMENT;
+        }
         *actual_size = look_ahead_size;
         pool_size += look_ahead_size;
 #ifdef DEBUG_CUDA_MALLOC
@@ -456,10 +470,20 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
             }
         }
         GGML_LOG_DEBUG(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
-        ggml_cuda_set_device(device);
-        CUDA_CHECK(cudaFree(ptr));
+        if (allocate) {
+            ggml_cuda_set_device(device);
+            CUDA_CHECK(cudaFree(ptr));
+        }
         pool_size -= size;
     }
+
+    bool alloc_memory() override {
+        return allocate;
+    }
+
+    size_t alloc_size() override {
+        return pool_size + last_alloc;
+    }
 };
 
 // pool with virtual memory
@@ -471,18 +495,24 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     CUdeviceptr pool_addr = 0;
     size_t pool_used = 0;
     size_t pool_size = 0;
+    bool allocate = true;
+    size_t last_alloc = 0;
     size_t granularity;
 #if defined(GGML_USE_HIP)
     std::vector<std::pair<CUdeviceptr, size_t>> mappings;
 #endif
 
-    explicit ggml_cuda_pool_vmm(int device) :
+    explicit ggml_cuda_pool_vmm(int device, bool alloc) :
         device(device),
-        granularity(ggml_cuda_info().devices[device].vmm_granularity) {
+        granularity(ggml_cuda_info().devices[device].vmm_granularity),
+        allocate(alloc) {
+        if (!allocate) {
+            pool_addr = (CUdeviceptr)CUDA_ALIGNMENT;
+        }
     }
 
     ~ggml_cuda_pool_vmm() {
-        if (pool_addr != 0) {
+        if (pool_addr != 0 && allocate) {
 #if defined(GGML_USE_HIP)
             // Workaround for https://github.com/ROCm/ROCR-Runtime/issues/285
             for (std::pair<CUdeviceptr, size_t> & mapping : mappings) {
@@ -509,35 +539,49 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
 
             GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
 
-            // allocate more physical memory
-            CUmemAllocationProp prop = {};
-            prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
-            prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-            prop.location.id = device;
-            CUmemGenericAllocationHandle handle;
-            CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0));
-
-            // reserve virtual address space (if not already reserved)
-            if (pool_addr == 0) {
-                CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
-            }
+            if (allocate) {
+                // allocate more physical memory
+                CUmemAllocationProp prop = {};
+                prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+                prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+                prop.location.id = device;
+                CUmemGenericAllocationHandle handle;
+                if (cuMemCreate(&handle, reserve_size, &prop, 0) != CUDA_SUCCESS) {
+                    last_alloc = reserve_size;
+                    throw std::bad_alloc();
+                }
 
-            // map at the end of the pool
-            CUdeviceptr start_ptr = (CUdeviceptr)((char *)(pool_addr) + pool_size);
-            CU_CHECK(cuMemMap(start_ptr, reserve_size, 0, handle, 0));
-#if defined(GGML_USE_HIP)
-            mappings.push_back({start_ptr, reserve_size});
-#endif
+                // reserve virtual address space (if not already reserved)
+                if (pool_addr == 0) {
+                    CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
+                }
 
-            // the memory allocation handle is no longer needed after mapping
-            CU_CHECK(cuMemRelease(handle));
+                // map at the end of the pool
+                CUdeviceptr start_ptr = (CUdeviceptr)((char *)(pool_addr) + pool_size);
+                if (cuMemMap(start_ptr, reserve_size, 0, handle, 0) != CUDA_SUCCESS) {
+                    last_alloc = reserve_size;
+                    CU_CHECK(cuMemRelease(handle));
+                    throw std::bad_alloc();
+                }
+
+                // the memory allocation handle is no longer needed after mapping
+                CU_CHECK(cuMemRelease(handle));
+
+                // set access
+                CUmemAccessDesc access = {};
+                access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+                access.location.id = device;
+                access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+                if (cuMemSetAccess((CUdeviceptr)((char *)(pool_addr) + pool_size), reserve_size, &access, 1) != CUDA_SUCCESS) {
+                    CU_CHECK(cuMemUnmap(start_ptr, reserve_size));
+                    last_alloc = reserve_size;
+                    throw std::bad_alloc();
+                }
 
-            // set access
-            CUmemAccessDesc access = {};
-            access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-            access.location.id = device;
-            access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
-            CU_CHECK(cuMemSetAccess((CUdeviceptr)((char *)(pool_addr) + pool_size), reserve_size, &access, 1));
+    #if defined(GGML_USE_HIP)
+                mappings.push_back({start_ptr, reserve_size});
+    #endif
+            }
 
             // add to the pool
             pool_size += reserve_size;
@@ -570,17 +614,27 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
         // all deallocations must be in reverse order of the allocations
         GGML_ASSERT(ptr == (void *) ((char *)(pool_addr) + pool_used));
     }
+
+    bool alloc_memory() override {
+        return allocate;
+    }
+
+    size_t alloc_size() override {
+        return pool_size + last_alloc;
+    }
+
 };
 #endif // defined(GGML_USE_VMM)
 
 std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int                  device,
-                                                                               [[maybe_unused]] int stream_no) {
+                                                                               [[maybe_unused]] int stream_no,
+                                                                               bool alloc) {
 #if defined(GGML_USE_VMM)
     if (ggml_cuda_info().devices[device].vmm) {
-        return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
+        return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device, alloc));
     }
 #endif // defined(GGML_USE_VMM)
-    return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
+    return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device, alloc));
 }
 
 // destroying a cuBLAS handle while a graph is being captured in a different thread can result in a CUDA error
@@ -764,11 +818,20 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
 }
 
 static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
+    return CUDA_ALIGNMENT;
 
     GGML_UNUSED(buft);
 }
 
+static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_noalloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+
+    void * dev_ptr = (void *)ggml_backend_cuda_buffer_type_get_alignment(buft);
+    ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr);
+
+    return ggml_backend_buffer_init(buft, {}, ctx, size);
+}
+
 static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
     size_t size = ggml_nbytes(tensor);
     int64_t ne0 = tensor->ne[0];
@@ -792,6 +855,7 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface
     /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
     /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
     /* .is_host          = */ NULL,
+    /* .noalloc_buffer   = */ ggml_backend_cuda_buffer_type_noalloc_buffer,
 };
 
 ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
@@ -3274,6 +3338,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
 
 static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
     bool & graph_evaluated_or_captured, bool & use_cuda_graph, bool & cuda_graph_update_required) {
+
     // flag used to determine whether it is an integrated_gpu
     const bool integrated = ggml_cuda_info().devices[cuda_ctx->device].integrated;
 
@@ -3410,6 +3475,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                     continue;
                 }
 
+                // When reserving, we are forcing CUDA graphs but this operation is not graph-safe so we need to skip it
+                if (reserving_graph && node->op == GGML_OP_MUL_MAT_ID && node->ne[2] != 1) {
+                    continue;
+                }
 
                 // start of fusion operations
                 static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
@@ -3754,6 +3823,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
 
 static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph, int batch_size) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    cuda_ctx->pool_set_alloc(true);
 
     ggml_cuda_set_device(cuda_ctx->device);
 
@@ -3829,6 +3899,77 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
     return GGML_STATUS_SUCCESS;
 }
 
+// This is used to skip operations that are not graph safe during the reservation process.
+bool reserving_graph = false;
+
+static enum ggml_status ggml_backend_cuda_graph_reserve(ggml_backend_t backend, ggml_cgraph * cgraph, bool alloc) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    cuda_ctx->pool_set_alloc(alloc);
+
+    #ifdef USE_CUDA_GRAPH
+    if (cuda_ctx->cuda_graph == nullptr) {
+        cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
+    }
+    #endif
+
+    ggml_cuda_set_device(cuda_ctx->device);
+
+    {
+        std::lock_guard<std::mutex> lock(ggml_cuda_lock);
+        ggml_cuda_lock_counter.fetch_add(1, std::memory_order_relaxed);
+    }
+
+    reserving_graph = true;
+
+    // Create CuBLAS handles early to avoid synchronous allocations during graph capture.
+    cuda_ctx->cublas_handle();
+
+    CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
+
+    enum ggml_status result = GGML_STATUS_SUCCESS;
+
+    try {
+        bool use_cuda_graph = false;
+        bool cuda_graph_update_required = false;
+        bool graph_evaluated_or_captured = false;
+
+        evaluate_and_capture_cuda_graph(cuda_ctx, cgraph, graph_evaluated_or_captured, use_cuda_graph, cuda_graph_update_required);
+    } catch (const std::exception &e) {
+        result = GGML_STATUS_FAILED;
+    }
+
+    cudaGraph_t graph;
+    CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &graph));
+    CUDA_CHECK(cudaGraphDestroy(graph));
+
+    reserving_graph = false;
+
+    {
+        std::lock_guard<std::mutex> lock(ggml_cuda_lock);
+        if (ggml_cuda_lock_counter.fetch_sub(1, std::memory_order_relaxed) == 1) {
+            ggml_cuda_lock_cv.notify_all();
+        }
+    }
+
+    return result;
+}
+
+static size_t ggml_backend_cuda_buffer_size(ggml_backend_t backend) {
+    ggml_backend_cuda_context * ctx = (ggml_backend_cuda_context *)backend->context;
+    size_t allocs = 0;
+    for (int i = 0; i < GGML_CUDA_MAX_STREAMS; i++) {
+        allocs += ctx->pool_get_alloc_size(i);
+    }
+    return allocs;
+}
+
+static void ggml_backend_cuda_reset(ggml_backend_t backend) {
+    ggml_backend_cuda_context * ctx = (ggml_backend_cuda_context *)backend->context;
+    for (int i = 0; i < GGML_CUDA_MAX_STREAMS; i++) {
+        ctx->pools[ctx->device][i] = NULL;
+    }
+}
+
 static void ggml_backend_cuda_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
@@ -4097,6 +4238,9 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .event_record            = */ ggml_backend_cuda_event_record,
     /* .event_wait              = */ ggml_backend_cuda_event_wait,
     /* .graph_optimize          = */ ggml_backend_cuda_graph_optimize,
+    /* .graph_reserve           = */ ggml_backend_cuda_graph_reserve,
+    /* .buffer_size             = */ ggml_backend_cuda_buffer_size,
+    /* .reset                   = */ ggml_backend_cuda_reset,
 };
 
 static ggml_guid_t ggml_backend_cuda_guid() {
