char-fork/llama.cpp
git clone https://git.t4t.associates/char-fork/llama.cpp
8300f1411
char/aliceai-t5-moe
files
| CMakeLists.txt | 724 B |
| README.md | 4.9 KiB |
| fit-params.cpp | 2.6 KiB |
| main.cpp | 125 B |
fit-params
llama.cpp binaries can automatically fit the projected memory use of a model to the free device memory available at runtime,
this is controlled using the CLI arguments starting with -fit/--fit.
Internally the code is calling llama_params_fit to adjust the llama_model_params and llama_context_params structs.
llama-fit-params is a simple utility that prints the CLI arguments corresponding to these adjustments to stdout.
Example usage:
# First, run llama-fit-params and store the results in a file: > ./build/bin/llama-fit-params--model /opt/models/qwen_3-30b3a-f16.gguf| tee args.txtggml_cuda_init: GGML_CUDA_FORCE_MMQ: noggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: noggml_cuda_init: found 1 CUDA devices:Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yesbuild: 6895 (4341dc8bc ) withcc (GCC ) 15.2.120250813 for x86_64-pc-linux-gnullama_params_fit_impl: projected to use 61807 MiB of device memory vs. 24077 MiB of free device memoryllama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 42444 MiBllama_params_fit_impl: context size reduced from 40960 to 4096- > need 3456 MiB less memory in totalllama_params_fit_impl: with only dense weights in device memory there is a total surplus of 16164 MiBllama_params_fit_impl: distributing layers across devices with overflow to next device/system memory:llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 4090): 48layers (34 overflowing),19187 MiB used, 1199 MiB freellama_params_fit: successfully fit params to free device memoryllama_params_fit: fitting params to free memory took 1.15 secondsPrinting fitted CLI arguments to stdout...-c 4096-ngl 48-ot blk\.14\.ffn_(up | down | gate )_(ch | )exps =CPU,blk\.15\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.16\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.17\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.18\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.19\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.20\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.21\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.22\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.23\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.24\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.25\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.26\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.27\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.28\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.29\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.30\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.31\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.32\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.33\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.34\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.35\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.36\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.37\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.38\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.39\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.40\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.41\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.42\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.43\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.44\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.45\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.46\.ffn_(up| down | gate )_(ch | )exps =CPU,blk\.47\.ffn_(up| down | gate )_(ch | )exps =CPU# Next, use those results for a llama.cpp binary: > catargs.txt | xargs ./build/bin/llama-server--model /opt/models/qwen_3-30b3a-f16.ggufggml_cuda_init: GGML_CUDA_FORCE_MMQ: noggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: noggml_cuda_init: found 1 CUDA devices:Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yesbuild: 6895 (4341dc8bc) withcc (GCC ) 15.2.120250813 for x86_64-pc-linux-gnusystem info: n_threads = 16, n_threads_batch = 16, total_threads = 32system_info: n_threads = 16 (n_threads_batch = 16) / 32| CUDA : ARCHS = 890| USE_GRAPHS = 1| PEER_MAX_BATCH_SIZE = 128| CPU : SSE3 = 1| SSSE3 = 1| AVX = 1| AVX_VNNI = 1| AVX2 = 1| F16C = 1| FMA = 1| BMI2 = 1| AVX512 = 1| AVX512_VBMI = 1| AVX512_VNNI = 1| AVX512_BF16 = 1| LLAMAFILE = 1| OPENMP = 1| REPACK = 1| main: binding port with default address family main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31 main: loading model srv load_model: loading model'/opt/models/qwen_3-30b3a-f16.gguf' llama_params_fit_impl: projected to use 19187 MiB of device memory vs. 24077 MiB of free device memory llama_params_fit_impl: will leave 1199> = 1024 MiB of free device memory, no changes needed llama_params_fit: successfully fit params to free device memory llama_params_fit: fitting params to free memory took 0.28 seconds [...] main: server is listening on http://127.0.0.1:8080 - starting the main loop srv update_slots: all slots are idle ^Csrv operator():operator(): cleaning up before exit...llama_memory_breakdown_print: | memory breakdown [MiB]| total free self model context compute unaccounted| llama_memory_breakdown_print: | - CUDA0 (RTX 4090)| 24077 = 945 + (19187 = 17904 + 384 + 898) + 3945| llama_memory_breakdown_print: | - Host| 58271 = 58259 + 0 + 12|