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Can the Annapurna Labs Alpine AL324 NAS (4 GB) run BGE Reranker v2 Gemma?

74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (far over memory limit)

Very slow (far over memory limit). At Q4_K_M it needs about 2.3 GB of 2.0 GB usable and makes one decision in about 129.2 s.

Best quant
Q4_K_M
Time per decision
129.2 s
Decisions per second
0.0/s
Input assumed
512 tokens
Returns
Probability or score

Best app to run it on the Annapurna Labs Alpine AL324 NAS (4 GB)

On Linux on ARM boards

llama.cpp

Command line + local API · runs on llama.cpp

  • Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
llama-server -hf mradermacher/bge-reranker-v2-gemma-GGUF:Q4_K_M --rerank

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download BGE Reranker v2 Gemma

Recommended for the Annapurna Labs Alpine AL324 NAS (4 GB): Q4_K_M. 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 Q4_K_M file (opens in a new tab)

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

llama-server -hf mradermacher/bge-reranker-v2-gemma-GGUF:Q4_K_M --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-gemma (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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 Very slowfar over memory limit
How well it runs on the Annapurna Labs Alpine AL324 NAS (4 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.33.3 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.4.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.4 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.15.0 / 15
Match score52.8 / 100

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

Every quantization on the Annapurna Labs Alpine AL324 NAS (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 2.3 GB 2.0 GB 129.2 s Very slowfar over memory limit 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 2.6 GB 2.0 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 2.9 GB 2.0 GB — Won't run 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 3.5 GB 2.0 GB — Won't run 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 5.8 GB 2.0 GB — Won't run 78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (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 and about 1 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does BGE Reranker v2 Gemma need on the Annapurna Labs Alpine AL324 NAS 4GB?

At Q4_K_M it needs about 2.3 GB. The Annapurna Labs Alpine AL324 NAS 4GB has 2.0 GB usable.

How fast is BGE Reranker v2 Gemma on the Annapurna Labs Alpine AL324 NAS 4GB?

It makes one decision in about 129.2 s at Q4_K_M, an estimate from the hardware and the size of the model.

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.

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