README.md

metadata

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 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. When you use it, attribute it as:

Data: Local LLM Advisor (localllmadvisor.com)

The full leaderboard (which also aggregates public community results under their upstream licenses) is at https://localllmadvisor.com/leaderboard.html, with a CSV download at https://localllmadvisor.com/data/leaderboard.csv. Source and tooling: https://github.com/mattgrandy/local-llm-advisor.

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.