willitrun·ai

Can GLM-4 9B run on Mac mini M4 64GB?

YES — Runs Great

B64Good
Estimated — low-sample bucket· few comparable runs

GLM-4 9B needs ~13.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 13.9 GB, 15.8 tok/s, Runs well
13.9 GB required46.1 GB available
30% VRAM used

Fit status

Runs well

Decode

15.8 tok/s

TTFT

12225 ms

Safe context

128K

Memory

13.9 GB / 46.1 GB

Memory breakdown

Weights5.5 GB
KV Cache0.6 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelsGLM-4 9B on Mac mini M4 64GB
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: 15.8 tok/s decode · 12.2s TTFT (warm) · 40 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well15.8 tok/s6668 ms128K
CodingBRuns well15.8 tok/s12225 ms128K
Agentic CodingBRuns well15.8 tok/s17782 ms128K
ReasoningBRuns well15.8 tok/s14448 ms128K
RAGBRuns well15.8 tok/s22228 ms128K

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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M126.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M126.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M126.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M126.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M121.7Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M111.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M92.4Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M87.7Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M75.3Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M74.7Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M74.7Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M47.8Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M47.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M43.8Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M38.5Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M36.4Offloads

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 mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB63
Q3_K_S
3
4.4 GB
LowB63
NVFP4
4
5.0 GB
MediumB63
Q4_K_M
4
5.5 GB
MediumB63
Q5_K_M
5
6.5 GB
HighB64
Q6_K
6
7.4 GB
HighB64
Q8_0
8
9.6 GB
Very HighB64
F16Best for your GPU
16
18.5 GB
MaximumB67

Get started

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

Run

ollama run glm4

アップグレードオプション

GLM-4 9Bを快適に動かすハードウェア

MacBook Pro M4 Max 96GBコスパ重視
96 GB Unified (+32)546 GB/s (+426)
B
Raises estimated decode speed by about 373%.74.7 tok/sデコード

Raises estimated decode speed by about 373%.

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

〜$2,499 MSRP

MacBook Pro M3 Max 128GBコスパ最良
128 GB Unified (+64)400 GB/s (+280)
B
Raises estimated decode speed by about 203%.47.8 tok/sデコード

Raises estimated decode speed by about 203%.

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

〜$2,499 MSRP

Mac Studio M3 Ultra 96GBAppleアップグレード
96 GB Unified (+32)819 GB/s (+699)
B
Raises estimated decode speed by about 603%.111 tok/sデコード

Raises estimated decode speed by about 603%.

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

〜$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run GLM-4 9B?

Yes, Mac mini M4 64GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 15.8 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 13.9 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 mini M4 64GB?

On Mac mini M4 64GB, GLM-4 9B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12225ms using Q4_K_M quantization.

Can Mac mini M4 64GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on Mac mini M4 64GB receives a B grade with 15.8 tok/s and 128K context.

What context window can GLM-4 9B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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 mini M4 64GB as fast as VRAM for GLM-4 9B?

Not always. Mac mini M4 64GB 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 mini M4 64GBSee all hardware for GLM-4 9B
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