How fast is eurollm-1.7b for local inference?

Across 5 community benchmark runs, eurollm-1.7b reaches up to 223.7 tok/s and averages 166.5 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Laptop GPU.

How much memory does eurollm-1.7b need?

The leanest observed configuration used about 2.4 GB of memory.

Which tools have been used to run eurollm-1.7b?

Benchmarks were submitted using Bionic. Results are community-contributed and updated as new runs arrive.

Model benchmarks

eurollm-1.7b local LLM performance

As of October 2026, eurollm-1.7b runs at up to 223.7 tok/s for local inference (best of 5 community benchmark runs across 1 GPU).

Bionic

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Model size

1.7B

Peak speed

223.7 tok/s

Average speed

166.5 tok/s

Avg PP

9107.8 tok/s

Min memory

2.4 GB

Max context

32,768 tokens

Avg output / run

136 tokens

Avg runtime / run

Avg quality

2.0

Benchmark runs

GPUs tested

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 5 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	0.8	2.0	4.4
Agent Workflow	0.0	2.8	7.1
Code Generation	0.0	1.0	4.2
Role Play & Narrative	0.0	2.3	8.3
Research & Analysis	0.0	1.8	3.8

Performance by hardware and tool

Every hardware/tool/quantization combination eurollm-1.7b has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 3060 Laptop GPU	Bionic	—	223.7 tok/s	166.5 tok/s	2.4 GB	32,768 tokens	2.0	5

Benchmark runs

All 5 eurollm-1.7b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

How fast is eurollm-1.7b for local inference?: Across 5 community benchmark runs, eurollm-1.7b reaches up to 223.7 tok/s and averages 166.5 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Laptop GPU.
How much memory does eurollm-1.7b need?: The leanest observed configuration used about 2.4 GB of memory.
Which tools have been used to run eurollm-1.7b?: Benchmarks were submitted using Bionic. Results are community-contributed and updated as new runs arrive.