MacBook Pro M3 Pro 36GB经济之选
36 GB Unified (+4)150 GB/s (+30)
B
Raises estimated decode speed by about 38%.21.8 tok/s 解码
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Can GLM-4 9B run on MacBook Pro M4 32GB?
YES — Runs Great
B67Good
Estimated — low-sample bucket· few comparable runs
GLM-4 9B needs ~10.5 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 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.
Select quantization to explore
Q4_K_M (Medium quality) — 10.5 GB, 15.8 tok/s, Runs well
10.5 GB required23.0 GB available
46% VRAM used
Fit status
Runs well
Decode
15.8 tok/s
TTFT
12225 ms
Safe context
128K
Memory
10.5 GB / 23.0 GB
Memory breakdown
Weights5.5 GB
KV Cache0.6 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsGLM-4 9B on MacBook Pro M4 32GB
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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 15.8 tok/s | 6668 ms | 128K |
| Coding | B | Runs well | 15.8 tok/s | 12225 ms | 128K |
| Agentic Coding | B | Runs well | 15.8 tok/s | 17782 ms | 128K |
| Reasoning | B | Runs well | 15.8 tok/s | 14448 ms | 128K |
| RAG | B | Runs well | 15.8 tok/s | 22228 ms | 128K |
Quantization options
How GLM-4 9B (9B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B67 |
NVFP4 | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | B67 |
Q5_K_M | 5 | 6.5 GB | High | B68 |
Q6_K | 6 | 7.4 GB | High | B69 |
Q8_0 | 8 | 9.6 GB | Very High | A70 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | A71 |
Get started
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4升级选项
能流畅运行 GLM-4 9B 的硬件
MacBook Pro M4 Max 36GB最佳性价比
36 GB Unified (+4)410 GB/s (+290)
A
Raises estimated decode speed by about 255%.56.1 tok/s 解码
Raises estimated decode speed by about 255%.
~$2,499 MSRP
MacBook Pro M4 Max 48GBApple 升级
48 GB Unified (+16)546 GB/s (+426)
A
Raises estimated decode speed by about 373%.74.7 tok/s 解码
Raises estimated decode speed by about 373%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 32GB run GLM-4 9B?
Yes, MacBook Pro M4 32GB 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 10.5 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 MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, 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 MacBook Pro M4 32GB run GLM-4 9B for coding?
For coding workloads, GLM-4 9B on MacBook Pro M4 32GB receives a B grade with 15.8 tok/s and 128K context.
What context window can GLM-4 9B use on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, 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 MacBook Pro M4 32GB as fast as VRAM for GLM-4 9B?
Not always. MacBook Pro M4 32GB 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.
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