GLM ties Kimi K3 at the top of the open-weight rankings, but 'run it locally' depends on which GLM — from the 106B Air that fits one card to the 744B GLM-5 that needs a 256GB Mac or the cloud. The honest map, with real numbers.
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GLM, from the Chinese lab Zhipu (Z.ai), sits at the top of the open-weight rankings — GLM-5.3 ties Kimi K3 at the top of the Artificial Analysis Intelligence Index (score 60) among open models as of September 2026 — but 'can I run it locally?' has wildly different answers depending on which GLM. The 106B GLM-4.5-Air fits a big single card; the 744B GLM-5 needs a 256GB Mac or a cloud rental. Here's the honest map of which GLM runs on what, with real numbers from the model cards. Sourced, no hands-on claims.
Which GLM can you actually run?
Added Nov 12, 2026: for a single consumer card, look at GLM-4.7-Flash, a 31.2B mixture-of-experts model (4 of 64 experts active, MIT license). Our VRAM engine puts it at about 19 GB at Q4_K_M with a 16K context, so it fits one 24 GB card. See what GLM-4.7-Flash needs and costs to run for every quant and the live rental price. The table below covers the bigger GLMs.
Because GLM's bigger models are mixture-of-experts, all the weights must sit in memory even though only the active experts run per token — so the constraint is capacity, and there are three ways to get it. Unified memory (a 128GB Strix Halo mini-PC or a Mac Studio) holds GLM-4.5-Air comfortably and GLM-5's 2-bit quant on a 256GB Mac, slowly. Multi-GPU (two or more 80GB cards, or MoE-offload to system RAM) is the other route, and a DGX Spark-class box handles the mid tier. Cloud is the flexible option when you don't want to buy a big-memory machine for an occasional run — rent a card that fits for cents to a few dollars an hour.
Frequently asked
Can I run GLM locally?
Yes, if you match the version to your hardware. GLM-4.5-Air (106B, ~60GB at 4-bit) runs on a single 80GB card or a 128GB unified-memory box. GLM-4.6 (2-bit ~135GB) and GLM-5 (2-bit ~241GB) need a large-memory machine — a 256GB Mac Studio, a multi-GPU rig, or the cloud. All figures are from the model cards and Unsloth's local-run docs (Sep 24, 2026).
Which is the most 'runnable' GLM?
GLM-4.5-Air. At 106B total / 12B active it quantises to about 60GB at 4-bit, so it fits a single 80GB or 96GB card, or a 128GB unified-memory mini-PC or Mac, with room for context. It's the version most people can actually run without a big-memory rig.
How much memory does GLM-5 need?
GLM-5 is 744B total / 40B active. Unsloth's 2-bit dynamic GGUF is about 241GB, which fits a 256GB unified-memory Mac (slowly), or a single 24GB card plus 256GB of system RAM with MoE offloading; the 1-bit quant is ~176GB. For usable speed most people rent multiple data-centre GPUs instead.
GLM's quality is real, but 'local' means picking the version your memory can hold: Air on one card, the big ones on unified memory or the cloud. Start with the best GPU for GLM-4.5-Air and its cost to run, then the Air hardware guide, GLM vs Qwen3, GLM vs DeepSeek V4, and the cheapest way to run GLM in the cloud. For the same shape on other models, see best GPU for Qwen3.
