Datasets:

LocalLLMAdvisor
/

local-llm-advisor-benchmarks

@@ -21,7 +21,7 @@ configs:
 # Local LLM Advisor: measured llama.cpp benchmarks
-540 `llama-bench` measurements taken by [Local LLM Advisor](https://localllmadvisor.com) on rented NVIDIA GPUs, covering 11 GPUs (rtx-3060-12, rtx-3090, rtx-4060, rtx-4060ti-16, rtx-4070, 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.
@@ -38,7 +38,7 @@ The full leaderboard (which also aggregates public community results under their
 - 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.
 ## Columns

 # 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.
 - 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