on

Can the Raspberry Pi 5 (2 GB) run BGE Reranker v2 M3?

72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. BGE Reranker v2 M3 needs about 1.3 GB even at Q8_0; the Raspberry Pi 5 (2 GB) has 1.2 GB usable.

Download BGE Reranker v2 M3

Recommended: Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in llama.cpp (llama-server --rerank), Lemonade and other llama.cpp servers with a rerank endpoint. No sign-in needed.

Download the Q8_0 file (opens in a new tab)

huggingface.co/gpustack/bge-reranker-v2-m3-GGUF (opens in a new tab)

llama-server -hf gpustack/bge-reranker-v2-m3-GGUF:Q8_0 --rerank

Original weights · Hugging Face

Full-precision files from BAAI, for Transformers, Sentence Transformers, Text Embeddings Inference, Infinity or vLLM.

huggingface.co/BAAI/bge-reranker-v2-m3 (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run
How well it runs on the Raspberry Pi 5 (2 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.9.7 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.0.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.0.0 / 15
Match score9.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the Raspberry Pi 5 (2 GB)

Quant Memory needed Available Speed Runs Quality Download
Q8_0 1.3 GB 1.2 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 1.9 GB 1.2 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 512-token input. Batching servers process many requests at once.

Hardware that can run it

Models that do run on the Raspberry Pi 5 (2 GB)

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

Was this page helpful?