Datasets:

iBlessi
/

rtx-5080-llm-throughput

license: cc-by-4.0
pretty_name: RTX 5080 Local LLM Throughput (Measured, Ollama)
language:
  - en
tags:
  - benchmark
  - gpu
  - llm-inference
  - nvidia
  - rtx-5080
  - ollama
size_categories:
  - n<1K

RTX 5080 Local LLM Throughput — Measured, Not Estimated

Measured decode and prefill tokens/second, load times, and VRAM residency for local LLMs on a single retail NVIDIA GeForce RTX 5080 (16 GB): gpt-oss:20b (MXFP4), Qwen 2.5 14B and 7B (Q4_K_M), and Llama 3.2 3B (Q4_K_M), at 4k and 16k context with ~500 and ~1,650-token prompts.

Method

Every figure is the median of 3 runs using ollama's native eval_count/eval_duration counters — never wall-clock division.
Every repetition prepends a unique nonce so each run pays a full prefill; without this, warm runs hit ollama's prompt cache and report ~60,000 tok/s cache-hit rates that measure nothing.
Generation pinned: temperature 0, seed 42, 256-token target output.
Model identity pinned by digest per row (a tag like qwen2.5:14b can silently move upstream).
Bench environment recorded per row: RTX 5080, driver 610.88, ollama 0.32.1, Windows 11, Ryzen 7 7800X3D.

Headline results

Model	Quant	Decode tok/s	Resident VRAM
Llama 3.2 3B	Q4_K_M	298–313	5.5 GiB (alone)
gpt-oss 20B	MXFP4	187–190	14.0–14.4 GiB
Qwen 2.5 7B	Q4_K_M	177–180	6.2–7.0 GiB
Qwen 2.5 14B	Q4_K_M	94–97	10.8–13.2 GiB

The 20.9B MXFP4 model decodes 2x faster than the dense 14B at Q4_K_M and matches the dense 7B — architecture and quantization beat parameter count. Decode speed holds flat from 4k to 16k context at these prompt sizes.

Caveats

Single retail unit, single driver, single runtime — these are this card's numbers. The VRAM column is whole-GPU nvidia-smi at measurement time and can include a previously-benched model still resident (rows measured alone are the lower readings).

Canonical source

Method detail, change log, and living updates: https://techfuelhq.com/data/rtx-5080-llm-throughput/

License

CC BY 4.0 — attribute as "TechFuelHQ RTX 5080 LLM Throughput Dataset" linking to the canonical page.