RTX 5080 Local LLM Throughput: Measured Tokens/Second Dataset

By LK Wood IV · Published 2026-08-12 · ~4 min read · St. Louis County, MO

The short version

This is a measured dataset, not a spec-sheet estimate: real decode and prefill tokens/second for local LLMs on the same retail RTX 5080 that runs this site’s open bench dataset. Three models spanning 3B to 20.9B parameters, two context windows, two prompt sizes, three repetitions per cell, ollama’s native counters, temperature 0.

Per-GPU pages exist elsewhere for the RTX 5090, 4090, and 5060 — none for the 5080 when this dataset was first captured (checked 2026-08-12). This page is the 5080 entry, from a card someone actually owns.

The numbers (capture 2026-08-12)

Paired values are the ~500-token / ~1,650-token prompt cells. Every value is the median of 3 runs.

*The starred Llama VRAM reading includes gpt-oss:20b still resident from the previous bench — see the FAQ. The 5,495 MiB row is the model alone.

Three readings worth naming:

• Decode speed is flat across context at these prompt sizes. 4k to 16k num_ctx moves gpt-oss:20b by less than 2 tok/s. Context capacity costs VRAM (Qwen: 10.8 → 13.2 GiB), not decode speed, until the prompt itself gets long.
• The 20B model is the surprise. gpt-oss:20b’s MXFP4 quantization decodes twice as fast as the dense 14B at Q4_K_M — architecture and quantization beat parameter count.
• Prefill is not the bottleneck at these sizes. Even the slowest model ingests ~5,000 tok/s; a 1,650-token prompt costs a third of a second.

Method, exactly

The capture script (in the site repository) drives ollama’s /api/generate and records its native eval_count/eval_duration and prompt_eval_count/prompt_eval_duration — the timers the runtime itself keeps — never wall-clock division. Each repetition prepends a unique nonce so every run pays a full prefill; without it, warm repetitions hit ollama’s prompt cache and report ~60,000 tok/s cache-hit rates that measure nothing (we caught exactly that in the first capture attempt and rejected those rows). Generation is pinned: temperature 0, seed 42, 256-token target output. A cell enters the CSV only when all 3 repetitions pass mechanical checks (enough tokens generated for a stable rate; a genuine full-prompt prefill observed).

Bench environment, recorded per row: NVIDIA GeForce RTX 5080 (16 GiB), driver 610.88, ollama 0.32.1, Windows 11, Ryzen 7 7800X3D. Model identity is pinned by digest in the CSV, because a tag like qwen2.5:14b can silently move.

Limitations, honestly

One retail card, one driver, one runtime. These are this unit’s numbers — silicon lottery, thermals, and runtime version all move results; that is why each row carries its digest, driver, and date instead of claiming to be a universal constant. The VRAM column is whole-GPU nvidia-smi at measurement time (see FAQ). llama.cpp direct, vLLM, and image-generation throughput are planned lanes, not covered yet.

Use the data (CC BY 4.0)

Download the CSV: rtx-5080-llm-throughput.csv. License: Creative Commons Attribution 4.0. Attribute as “TechFuelHQ RTX 5080 LLM Throughput Dataset” linking to this page. Sizing a build for local AI? Pair these measured rows with the LLM VRAM calculator and the LLM speed calculator — the estimators tell you what should fit; this table shows what one real card actually did.

Change log

• 1.0.0 (2026-08-12) — first capture: gpt-oss:20b, Qwen 2.5 14B, Llama 3.2 3B × 4k/16k context × ~500/~1,650-token prompts, 3 reps per cell, ollama 0.32.1, driver 610.88. First-capture defect worth recording: warm-run prompt-cache contamination was detected and eliminated by per-rep nonces before any row entered the dataset.