Can MacBook Air M2 24 GB run GLM-4.6V-Flash (10B)?

24 GB unified · 100 GB/s memory bandwidth · GLM-4.6V-Flash (10B) has 10.3B parameters.

Short answer

Yes, comfortable for chat; borderline for coding in a repository.

Best quantization for chat: 4-bit (5.4 GB of weights) on LM Studio. Generation: 11–15 tok/s, leaving about 9.8 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Comfortable	4-bit	11–15 tok/s	≈6 s	8K	Details
Coding questionswith Continue	Acceptable	4-bit	11–15 tok/s	≈40 s	16K	Details
Coding in a repositorywith Aider	Borderline	5-bit	8–11 tok/s	≈2 min	32K	Details
Agentic codingwith OpenCode	Borderline	5-bit	8–11 tok/s	≈3 min	32K	Details
Long documents (64K)with Open WebUI	Borderline	5-bit	7–10 tok/s	≈12 min	64K	Details

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 4-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/GLM-4.6V-Flash-GGUF:Q4_K_M

Downloads 6.17 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

Other models for MacBook Air M2 24 GB

The best-rated models for chat on this machine.

Everything MacBook Air M2 24 GB can run

Cheapest machines for GLM-4.6V-Flash (10B)

Hardware rated comfortable or better for chat, lowest price first.

Same chip, other memory: 8 GB, 16 GB

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Open sourceCheck my exact setup