--- license: cc-by-4.0 pretty_name: Local LLM Advisor - measured llama.cpp benchmarks language: - en tags: - llama.cpp - benchmark - gguf - local-llm - inference - gpu size_categories: - n<1K configs: - config_name: default data_files: own_measurements.csv - config_name: multigpu data_files: own_measurements_multigpu.csv --- # Local LLM Advisor: measured llama.cpp benchmarks 586 `llama-bench` measurements taken by [Local LLM Advisor](https://localllmadvisor.com) on rented NVIDIA GPUs, covering 12 GPUs (rtx-3060-12, rtx-3090, rtx-4060, rtx-4060ti-16, rtx-4070, rtx-5060ti-16, rtx-4090, rtx-5070, rtx-5070ti, rtx-5080, rtx-5090, rtx-pro-6000), 13 GGUF models and context depths of 0, 4096, 16384, 32768, 65536, 131072 tokens (depths above 16384 only for the RTX 3090 and RTX 4090, Q4_K_M, models that fit). Every row is our own run; **no third-party benchmark rows are included** in this dataset. `own_measurements_multigpu.csv` (config `multigpu`) holds 21 further rows from a dual RTX 3090 box (`device_count` 2, `hw_id` `2x-rtx-3090`, llama.cpp default layer split across both GPUs, CUDA 12.8, b11658, depths 0/4096/16384): six Q8_0 models of 25-37 GB plus Llama 3.3 70B Q4_K_M (about 19 tok/s generation). They are kept separate from the single-GPU file and are not used for the site's per-card medians. ## Attribution (CC BY 4.0) This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). When you use it, attribute it as: > Data: Local LLM Advisor ([localllmadvisor.com](https://localllmadvisor.com)) The full leaderboard (which also aggregates public community results under their upstream licenses) is at , with a CSV download at . Source and tooling: . ## Method - Tool: `llama-bench` from llama.cpp build b11658 (commit 3e4dfa8f3), CUDA 12.8 build, flash attention on, all layers on GPU (`-ngl 99`), 3 repetitions (`-r 3`), single GPU. - `pp_tps` = prompt processing tokens/s (`pp_n` prompt tokens, default 512); `tg_tps` = generation tokens/s (`tg_n` tokens, default 128). Rows with `depth` > 0 measure speed with that many tokens already in the context. - Rented machines differ in CPU and RAM; per-row details (driver, CPU, GGUF file, standard deviations) are in the `notes` column. - Dates of runs: 2026-10-16, 2026-10-17, 2026-10-18, 2026-10-21. ## Columns | column | meaning | |---|---| | hw_id | Local LLM Advisor hardware id | | device_raw | GPU name as reported by the driver | | device_count | number of GPUs used | | backend | llama.cpp backend (CUDA) | | model, quant | model name and GGUF quantization | | params_b, size_gib | parameters (billions) and GGUF file size (GiB) | | fa, ngl, threads, ubatch | flash attention, GPU layers, CPU threads, micro-batch | | depth | tokens already in context before the measurement | | pp_n, pp_tps | prompt tokens and prompt-processing speed | | tg_n, tg_tps | generated tokens and generation speed | | build, build_num | llama.cpp commit and build number | | n_samples | repetitions averaged by llama-bench | | date, notes | run date and free-text run details | ## Caveats Single runs of one machine per GPU model on rented hosts: treat numbers as indicative. Speeds depend on driver, CPU, thermal and power limits.