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Can the BeagleBone AI-64 (4 GB) run GLM-4.6 (355B-A32B)?

58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. GLM-4.6 (355B-A32B) needs about 147 GB even at Q2_K; the BeagleBone AI-64 (4 GB) has 2.8 GB usable.

Download GLM-4.6 (355B-A32B)

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

Ollama

Ollama only offered this as a cloud model, and has since retired it. To run it locally, use the GGUF files below with llama.cpp or LM Studio.

Quantized files · GGUF

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

Download the Q2_K files (opens in a new tab)

huggingface.co/unsloth/GLM-4.6-GGUF (opens in a new tab)

ollama run hf.co/unsloth/GLM-4.6-GGUF

Original weights · Hugging Face

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

huggingface.co/zai-org/GLM-4.6 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/zai-org/GLM-4.5 (opens in a new tab)

git clone https://github.com/zai-org/GLM-4.5

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

How it’s rated

Quality 58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 7/100 (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.7.9 / 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.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 score7.9 / 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 147 GB 2.8 GB — Won't run 58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q3_K_M 179 GB 2.8 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q4_K_M 222 GB 2.8 GB — Won't run 79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q5_K_M 257 GB 2.8 GB — Won't run 81Great quality (A) · 81 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 · folder (opens in a new tab)
Q6_K 296 GB 2.8 GB — Won't run 83Great quality (A) · 83 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q8_0 382 GB 2.8 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 · folder (opens in a new tab)
F16 717 GB 2.8 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 · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K 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 GLM-4.6 (355B-A32B) need on the BeagleBone AI-64 4GB?

Even at Q2_K it needs about 147 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 GLM-4.6 (355B-A32B)?

M3 Ultra 512GB, M3 Ultra 256GB and M2 Ultra 192GB 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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