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authorJohannes Gäßler <johannesg@5d6.de>2023-06-14 19:47:19 +0200
committerGitHub <noreply@github.com>2023-06-14 19:47:19 +0200
commit254a7a7a5ff4c874ff8488f1f5cbdd7e9c89d682 (patch)
tree65f35a2d189f3cf6f1f625b2acb343c2dd77790d /llama.h
parent92549202659fc23ba9fec5e688227d0da9b06b40 (diff)
CUDA full GPU acceleration, KV cache in VRAM (#1827)
* Fixed CUDA RoPE * ggml_cuda_mul_mat_vec_p021 * ggml_cuda_scale * ggml_cuda_diag_mask_inf * ggml_is_permuted * ggml_cuda_cpy * flatten rows for ggml_cuda_op * Added a --low-vram option * Fixed Windows performance * Fixed LLAMA_CUDA_DMMV_Y > 1 for WizardLM
Diffstat (limited to 'llama.h')
-rw-r--r--llama.h1
1 files changed, 1 insertions, 0 deletions
diff --git a/llama.h b/llama.h
index 61f6c86..6429226 100644
--- a/llama.h
+++ b/llama.h
@@ -77,6 +77,7 @@ extern "C" {
int n_gpu_layers; // number of layers to store in VRAM
int main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
+ bool low_vram; // if true, reduce VRAM usage at the cost of performance
int seed; // RNG seed, -1 for random
bool f16_kv; // use fp16 for KV cache