Can GLM-4 9B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

B67Good

Estimated from fit model

GLM-4 9B needs ~34.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~111 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 34.6 GB, 111.0 tok/s, Runs well

34.6 GB required184.3 GB available

19% VRAM used

Fit status

Runs well

Decode

111.0 tok/s

TTFT

1745 ms

Safe context

128K

Memory

34.6 GB / 184.3 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsGLM-4 9B on Mac Studio M3 Ultra 256GB
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: 111.0 tok/s decode · 1.7s TTFT (warm) · 277 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	B	Runs well	111.0 tok/s	952 ms	128K
Coding	B	Runs well	111.0 tok/s	1745 ms	128K
Agentic Coding	B	Runs well	111.0 tok/s	2538 ms	128K
Reasoning	B	Runs well	111.0 tok/s	2062 ms	128K
RAG	B	Runs well	111.0 tok/s	3173 ms	128K

Inference speed

GLM-4 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GLM-4 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	121.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	111.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	92.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	87.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	75.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	74.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	74.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	47.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	38.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	36.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B58
Q3_K_S	3	4.4 GB	Low	B58
NVFP4	4	5.0 GB	Medium	B58
Q4_K_M	4	5.5 GB	Medium	B58
Q5_K_M	5	6.5 GB	High	B58
Q6_K	6	7.4 GB	High	B58
Q8_0	8	9.6 GB	Very High	B59
F16Best for your GPU	16	18.5 GB	Maximum	B59

Get started

Copy-paste commands to run GLM-4 9B on your machine.

Run

ollama run glm4

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run GLM-4 9B?

Yes, Mac Studio M3 Ultra 256GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 111.0 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 34.6 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-4 9B?

The recommended quantization for GLM-4 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-4 9B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, GLM-4 9B achieves approximately 111.0 tokens per second decode speed with a time-to-first-token of 1745ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on Mac Studio M3 Ultra 256GB receives a B grade with 111.0 tok/s and 128K context.

What context window can GLM-4 9B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, GLM-4 9B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for GLM-4 9B?

Not always. Mac Studio M3 Ultra 256GB 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.

See all results for Mac Studio M3 Ultra 256GB See all hardware for GLM-4 9B

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