Can glm 4 9b chat 1m run on MacBook Air M2 16GB?

Q: Is unified memory on MacBook Air M2 16GB as fast as VRAM for glm 4 9b chat 1m?

Not always. MacBook Air M2 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

YES — Runs Great

C50Usable

Estimated from fit model

glm 4 9b chat 1m needs ~9.2 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.2 GB, 11.8 tok/s, Runs well

9.2 GB required11.5 GB available

80% VRAM used

Fit status

Runs well

Decode

11.8 tok/s

TTFT

16352 ms

Safe context

52K

Memory

9.2 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on MacBook Air M2 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.8 tok/s decode · 16.4s TTFT (warm) · 30 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	11.8 tok/s	8919 ms	52K
Coding	C	Runs well	11.8 tok/s	16352 ms	52K
Agentic Coding	C	Tight fit	11.8 tok/s	23784 ms	52K
Reasoning	C	Runs well	11.8 tok/s	19325 ms	52K
RAG	C	Tight fit	11.8 tok/s	29730 ms	52K

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run glm 4 9b chat 1m on your machine.

Run

lms load hf-bartowski--glm-4-9b-chat-1m-gguf && lms server start

Upgrade options

Hardware that runs glm 4 9b chat 1m well

MacBook Pro M3 Pro 18GBBudget pick

18 GB Unified (+2)150 GB/s (+50)

Raises estimated decode speed by about 69%.19.9 tok/s decode

Raises estimated decode speed by about 69%.

~$1,999 MSRP

MacBook Pro M4 Pro 24GBBest value

24 GB Unified (+8)273 GB/s (+173)

Raises estimated decode speed by about 198%.35.2 tok/s decode

Raises estimated decode speed by about 198%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run glm 4 9b chat 1m?

Yes, MacBook Air M2 16GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 11.8 tok/s.

How much VRAM does glm 4 9b chat 1m need?

glm 4 9b chat 1m (9B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for glm 4 9b chat 1m?

The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.

What speed will glm 4 9b chat 1m run at on MacBook Air M2 16GB?

On MacBook Air M2 16GB, glm 4 9b chat 1m achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16352ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on MacBook Air M2 16GB receives a C grade with 11.8 tok/s and 52K context.

What context window can glm 4 9b chat 1m use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, glm 4 9b chat 1m can safely use up to 52K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for glm 4 9b chat 1m?

See all results for MacBook Air M2 16GB See all hardware for glm 4 9b chat 1m

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