GLM vs Qwen3 for local use: which top open model to actually run

Qwen3-30B-A3B fits a 24GB card at ~17.5GB; GLM-4.5-Air needs ~60GB. GLM ranks a touch higher; Qwen3 runs on far less. How to choose, with sourced numbers.

TensorSep 24Updated Sep 247 min readWeb story

Flat illustration of a person comparing GLM and Qwen3 models for local use, on a teal background

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GLM and Qwen3 are the two Chinese open-weight families at the top of the 2026 rankings, and for local use they pull in different directions. Qwen3 has a genuinely tiny-footprint MoE — Qwen3-30B-A3B runs in about 17.5GB — while the most local GLM, GLM-4.5-Air, needs about 60GB at 4-bit. GLM tends to rank higher on quality; Qwen3 fits a single consumer card. Here's how to choose, with sourced numbers and no hands-on claims.

For running locally, Qwen3-30B-A3B (30B total / 3B active) fits in about 17.5GB — it runs on a single 24GB consumer card. GLM-4.5-Air (106B / 12B active) needs about 60GB at 4-bit, so it wants an 80GB card or a 128GB unified-memory box. GLM's flagship GLM-5.3 ties Kimi K3 at the top of the open-weight quality rankings (Artificial Analysis Intelligence Index, score 60, Sep 2026), but Qwen3 is far easier to run on modest hardware. Choose Qwen3-30B-A3B for a single consumer GPU, GLM-4.5-Air (or larger GLM) when you have the memory and want the extra quality.

Qwen3-30B-A3B: 30B / 3B active, ~17.5GB — fits a single 24GB card.
GLM-4.5-Air: 106B / 12B active, ~60GB at 4-bit — wants 80GB or 128GB unified.
Quality: GLM-5.3 ties Kimi K3 at the top of open-weight rankings; Qwen3 is close and lighter.
Both are MoE with small active-parameter counts, so both are fast for their size.
Rule of thumb: modest hardware → Qwen3; more memory + top quality → GLM.

The local footprint, side by side

GLM vs Qwen3 — local memory · verified 24 Sep 2026

Qwen3-30B-A3B

Params (total / active): 30B / 3B
Smallest practical VRAM: ~17.5GB
Single-card home: One 24GB card (3090/4090)

GLM-4.5-Air

Params (total / active): 106B / 12B
Smallest practical VRAM: ~60GB (4-bit)
Single-card home: One 80GB card / 128GB unified

GLM-5 (flagship)

Params (total / active): 744B / 40B
Smallest practical VRAM: ~241GB (2-bit)
Single-card home: 256GB Mac / multi-GPU / cloud

	Params (total / active)	Smallest practical VRAM	Single-card home
Qwen3-30B-A3B	30B / 3B	~17.5GB	One 24GB card (3090/4090)
GLM-4.5-Air	106B / 12B	~60GB (4-bit)	One 80GB card / 128GB unified
GLM-5 (flagship)	744B / 40B	~241GB (2-bit)	256GB Mac / multi-GPU / cloud

The practical read: if you have a 24GB card, Qwen3-30B-A3B just works, and it's excellent — see our best GPU for Qwen3 guide. If you have an 80GB card or a 128GB unified-memory box and want a notch more quality, GLM-4.5-Air is the pick. And if you want GLM-5's frontier quality, that's unified-memory-or-cloud territory. Match any of them to a card with our cost-to-run tool.

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Frequently asked

Is GLM or Qwen3 better for local use?

It depends on your hardware. Qwen3-30B-A3B runs in ~17.5GB on a single 24GB card, so it's the easier local model; GLM-4.5-Air needs ~60GB but ranks a little higher on quality, and GLM's flagship ties Kimi K3 at the top of the open-weight index. Pick Qwen3 for modest hardware, GLM when you have more memory and want the extra quality.

Can I run GLM on a 24GB card like a Qwen3 model?

Not the same way. Qwen3-30B-A3B fits a 24GB card directly at ~17.5GB; the smallest GLM (4.5-Air) needs ~60GB at 4-bit, so on a single 24GB card you'd need heavy MoE offload to system RAM, which is slow. For a 24GB card, Qwen3-30B-A3B is the far better fit.

Which has the longer context?

Both are generous: Qwen3 supports very long contexts (256K natively, extendable further), and GLM-4.5-Air is 128K with GLM-5 at 200K. For most local use either is more than enough; longer contexts cost extra memory on top of the weights.

Qwen3 for a single consumer card, GLM for more memory and top-end quality — that's the split. See best GPU for Qwen3, the GLM local pillar, the GLM-4.5-Air hardware guide, and GLM vs DeepSeek V4.

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Local AI & Automation Editor

Tensor

I'm US-based, I run more models at home than I'll admit to, and I've quantized more than I've finished reading about. I write about running AI on your own hardware and, lately, about what it costs a company to do the same — tokens per day, GPUs per month, and the GDPR questions nobody's sales deck answers.

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