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llamacpp-3653e6d-cuda12.8-universal-sm61to120-ubuntu22.04-x86_64.tar.zst
774 MB compressed / 1.2 GB extracted.1hf download gbuzhf/llamacpp-prebuilt <file> --local-dir .
2mkdir -p llama.cpp/build && tar -C llama.cpp/build -xf <file> # -> llama.cpp/build/binprovision-apex.sh Phase 1 finds every target present and skips the build entirely.| arch | hardware |
|---|---|
| 61 | GTX 10xx, P40 |
| 70 | V100 |
| 75 | RTX 20xx, T4 |
| 80 | A100 |
| 86 | RTX 30xx, A10, A6000 |
| 89 | RTX 40xx, L40S |
| 90 | H100, H200 |
| 100 | B200 |
| 120 | RTX 50xx, RTX PRO 6000 |
GGML_NATIVE=OFF with GGML_CPU_ALL_VARIANTS=ON: 14 per-ISA backends
(sse42 through sapphirerapids, znver*), selected at runtime. Nothing is pinned to
the machine that compiled it.libcudart, libcublas, libcublasLt ship inside, so
the artifact does not require a matching CUDA image. Only the NVIDIA driver must be
new enough for the 12.8 runtime.GGML_BACKEND_DL=ON), so a box with no GPU falls
back to the CPU backends rather than failing to start.llama-quantize, llama-imatrix, llama-gguf-split, llama-server,
llama-perplexity, llama-cli, plus shared libraries.convert_hf_to_gguf.py and gguf-py, which are pure Python and come
from the llama.cpp clone.3653e6d, CUDA 12.8.93, Ubuntu 22.04 (glibc 2.35), built on a 256-core
EPYC 7V13 in ~9 minutes.sm_120 — nvcc rejects the architecture and
cmake aborts at enable_language(CUDA). A "universal" build made on the common
nvidia/cuda:12.4.1-devel image is silently Blackwell-less. 12.8 is the floor.