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.

Lena Fischer1h agoUpdated 1h ago7 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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AI & Local Compute Editor

Lena Fischer

Lena runs more GPUs at home than she'll admit to and has quantized more models than she's finished reading about. She writes about running AI on your own hardware — what actually fits, what's genuinely fast, and what the polished cloud demos quietly leave out.

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