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Can the Raspberry Pi 4 (2 GB) run Jina Reranker v3?

79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (over memory limit)

Very slow (over memory limit). At Q4_K_M it needs about 1.3 GB of 1.2 GB usable and makes one decision in about 10.4 s.

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

Best app to run it on the Raspberry Pi 4 (2 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 jinaai/jina-reranker-v3-GGUF:Q4_K_M --rerank
  • Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Jina Reranker v3

Recommended for the Raspberry Pi 4 (2 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/jinaai/jina-reranker-v3-GGUF (opens in a new tab)

llama-server -hf jinaai/jina-reranker-v3-GGUF:Q4_K_M --rerank

Original weights · Hugging Face

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

huggingface.co/jinaai/jina-reranker-v3 (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 79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (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 slowover memory limit
How well it runs on the Raspberry Pi 4 (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.35.9 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.10.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.5 / 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 score62.4 / 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 Raspberry Pi 4 (2 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 1.3 GB 1.2 GB 10.4 s Very slowover memory limit 79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 1.4 GB 1.2 GB 10.4 s Very slowfar over memory limit 81Great quality (A) · 81 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 1.4 GB 1.2 GB 10.4 s Very slowfar over memory limit 83Great quality (A) · 83 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 1.6 GB 1.2 GB — Won't run 84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 2.1 GB 1.2 GB — Won't run 84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (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

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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