Smeltcore GPU measurements

Raw VRAM and timing measurements of AI models running locally on consumer GPUs, made by the operator of smeltcore.com on his own hardware. Every benchmark on smeltcore.com that says measured by us points at a session folder in this repository, so a reader can check the number against the trace it came from.

This is not a leaderboard. It is the evidence behind specific claims — "this model fits on this card in this configuration" — published so that the claim can be audited rather than trusted.

The table

runs.csv holds one row per generation across every session. The dataset viewer above shows it; filter by model_slug and gpu_slug. The full column list and the rules behind it are in SCHEMA.md.

Read these before comparing numbers

• Units are MiB, as the instrument reports them. Nothing is converted to GB in this table. nvidia-smi reports MiB; NVML reports bytes; neither is decimal gigabytes. Convert at the point of use.
• These are whole-card figures. They include whatever a monitor or another program holds on the card. display_attached says whether a monitor was driven by the GPU. idle_device_mib is the card's memory just before the run; peak_device_mib − idle_device_mib estimates the workload's share, assuming the rest held steady.
• An empty cell means "not measured", never zero. One session has no idle baseline; its idle column is empty rather than guessed.
• A failed run is a row. An out-of-memory run appears with ok = false and failure = oom. A missing combination means it was not attempted.
• Cold and warm runs differ. cold_start says whether models were unloaded first. A model already resident from a previous job changes the memory profile of the next one.
• Unreplicated: one rig, one operator. These are single-machine measurements. They show that something ran, with what, and at what cost on that machine — not a population average.

Layout

runs.csv                      every run, one row each — the table the viewer shows
SCHEMA.md                     the columns, and why each rule exists
sessions/<session_id>/
    README.md                 what was measured and what it found
    session.json              environment, instrument, and caveats
    raw/                      traces, the runtime's own output, and the harness that ran
harness/                      the current measurement harnesses

Sessions

session	model	card	runs	result
2026-08-13_ltx-2-5_rtx-5060-ti	LTX-2.5 (video)	RTX 5060 Ti 16GB	15	no OOM, up to 1920x1088 and 193 frames
2026-09-10_yue2-3b_rtx-5060-ti	YuE2-3B (music)	RTX 5060 Ti 16GB	4	no OOM, including full context on two CFG branches
2026-09-14_krea-2_rtx-5060-ti	Krea 2 Turbo FP8 (image)	RTX 5060 Ti 16GB	5	no OOM, up to 1920x1088
2026-09-14_wan-2-2_rtx-5060-ti	Wan 2.2 TI2V-5B (video)	RTX 5060 Ti 16GB	6	no OOM, 720p 5 s on FP16 and Q8_0 GGUF
2026-09-14_qwen3-8-27b_rtx-5060-ti	Qwen3.8-27B (vision LLM)	RTX 5060 Ti 16GB	5	fits in VRAM with vision at 32K and text-only at 65K
2026-09-21_qwen-image-2-1_rtx-5060-ti	Qwen-Image-2.1 (image)	RTX 5060 Ti 16GB	8	no OOM, up to 2752x1536; PyTorch cu128, so int8 ran on the slow path
2026-09-22_qwen-image-2-1_rtx-5060-ti	Qwen-Image-2.1 (image)	RTX 5060 Ti 16GB	5	cu130 rerun: template 1024x1024 in 21 s, int8 1.7-2.3x faster than bf16

Licence

CC BY-SA 4.0, matching Smeltcore/gpu-compatibility. The model weights measured here carry their own licences; nothing from them is redistributed.