Granite Embedding Reranker English R2 on Marvell ARMADA 8040 NAS (4 GB)

Can the Marvell ARMADA 8040 NAS (4 GB) run Granite Embedding Reranker English R2?

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 0.8 GB of 2.0 GB usable and makes one decision in about 6.7 s.

Best precision: Q8_0
Time per decision: 6.7 s
Decisions per second: 0.1/s
Input assumed: 512 tokens
Returns: Probability or score

Best app to run it on the Marvell ARMADA 8040 NAS (4 GB)

On Linux on ARM boards

Infinity

Inference server · runs on PyTorch, ONNX, CTranslate2

Local API for rerankers and classifiers, including models TEI does not support.

infinity_emb v2 --model-id ibm-granite/granite-embedding-reranker-english-r2

LocalAI One OpenAI-compatible server for GGUF guard models and rerankers together.

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 Granite Embedding Reranker English R2

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-embedding-reranker-english-r2 (opens in a new tab)

No sign-in needed.

Links checked 5 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (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 Very slow: How well it runs on the Marvell ARMADA 8040 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.	30.6 / 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.	25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.	1.8 / 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 score	72.4 / 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 Marvell ARMADA 8040 NAS (4 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	0.8 GB	2.0 GB	6.7 s	Very slow	68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	0.9 GB	2.0 GB	7.7 s	Very slow	68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

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

Other hardware that runs it

68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 5.3 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 5.6 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) F16 · 6.6 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 64 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) F16 · 19 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 9.7 ms · Runs great

Quick answers

How much memory does Granite Embedding Reranker English R2 need on the Marvell ARMADA 8040 NAS 4GB?

At Q8_0 it needs about 0.8 GB. The Marvell ARMADA 8040 NAS 4GB has 2.0 GB usable.

How fast is Granite Embedding Reranker English R2 on the Marvell ARMADA 8040 NAS 4GB?

It makes one decision in about 6.7 s at Q8_0, 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.