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Can the BeagleBone AI-64 (4 GB) run OLMoE 1B-7B Instruct?

18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. OLMoE 1B-7B Instruct needs about 4.1 GB even at Q2_K; the BeagleBone AI-64 (4 GB) has 2.8 GB usable.

Download OLMoE 1B-7B Instruct

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K file (opens in a new tab)

huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF (opens in a new tab)

ollama run hf.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF:Q2_K

Original weights · Hugging Face

Full-precision files from Ai2, for fine-tuning, vLLM, Transformers.

huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ai2. The weights themselves download from Hugging Face.

github.com/allenai/OLMoE (opens in a new tab)

git clone https://github.com/allenai/OLMoE

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

How it’s rated

Quality 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 BeagleBone AI-64 (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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.2.5 / 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.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score2.5 / 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 BeagleBone AI-64 (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 4.1 GB 2.8 GB — Won't run 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 4.8 GB 2.8 GB — Won't run 21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 5.6 GB 2.8 GB — Won't run 24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 6.3 GB 2.8 GB — Won't run 25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 7.0 GB 2.8 GB — Won't run 25Basic quality (D) · 25 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 8.7 GB 2.8 GB — Won't run 26Basic quality (D) · 26 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 15.2 GB 2.8 GB — Won't run 26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

Models that do run on the BeagleBone AI-64 (4 GB)

Quick answers

How much memory does OLMoE 1B-7B Instruct need on the BeagleBone AI-64 4GB?

Even at Q2_K it needs about 4.1 GB; the BeagleBone AI-64 4GB has 2.8 GB usable.

What can the BeagleBone AI-64 4GB run instead?

MiniCPM5 2B, LFM3 1.7B and Spark-X2.6 4B run best on it; the page lists more, each with its speed.

What hardware can run OLMoE 1B-7B Instruct?

RX 7900 XTX 24GB, Arc B580 12GB and RTX 3060 12GB run it; the page lists more, each with its speed.

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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