__func__); backend = ggml_backend_cuda_init(); // init device 0if (!backend) {fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); }#endif// if there aren't GPU Backends fallback to CPU backendif (!backend) { backend = ggml_backend_cpu_init();...
ggml_backend_t backend = NULL; #ifdef GGML_USE_CUDA fprintf(stderr, "%s: using CUDA backend\n", __func__); backend = ggml_backend_cuda_init(0); // init device 0 if (!backend) { fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); } #endif // if the...
ggml_backend_t backend = NULL; #ifdef GGML_USE_CUDA fprintf(stderr, "%s: using CUDA backend\n", __func__); backend = ggml_backend_cuda_init(0); // init device 0 if (!backend) { fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); } #endif // if the...
backend =ggml_backend_cuda_init(0);// init device 0 if(!backend) { fprintf(stderr,"%s: ggml_backend_cuda_init() failed\n", __func__); } #endif // if there aren't GPU Backends fallback to CPU backend if(!backend) {
ggml_backend_t backend=NULL;#ifdef GGML_USE_CUDAfprintf(stderr,"%s: using CUDA backend\n",__func__);backend=ggml_backend_cuda_init(0);//init device0if(!backend){fprintf(stderr,"%s: ggml_backend_cuda_init() failed\n",__func__);}#endif//ifthere aren't GPU Backends fallback ...
for ggml-cuda.cu char padding[12]; }; static const int64_t GGML_NE_WILDCARD = -1; static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor); // the compute plan that needs to be prepared for ggml_graph_compute() // since https://github.com/ggerganov/ggml...
Name and Version ./llama-cli --version ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4070 Laptop GPU, compute capability 8.9, VMM: ye...
llm_load_print_meta: EOT token = 128009 '<|eot_id|>' ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes ggml_cuda_init: found 1 ROCm devices: Device 0: AMD Radeon RX 7900 XT, compute capability 11.0, VMM: no llm_load_tensors: ggml ctx size...
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes ggml_cuda_init: found 5 CUDA devices: Device 0: Tesla P100-PCIE-16GB, compute capability 6.0, VMM: yes Device 1: Tesla P100-PCIE-16GB, compute capability 6.0, VMM: yes Device 2: Tesla P100-PCIE-16GB, compute capability 6.0, VMM: yes Devic...
Name and Version llama-server --version ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 1080 Ti, compute capability 6.1, VMM: yes versi...