MacBook Pro M4 Max 36GBOpção econômica
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 101%.51.3 tok/s decodificação
Raises estimated decode speed by about 101%.
~$2,499 MSRP
Can glm 4 9b chat 1m run on MacBook Pro M2 Pro 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
glm 4 9b chat 1m needs ~10.9 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 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) — 10.9 GB, 25.5 tok/s, Runs well
10.9 GB required23.0 GB available
47% VRAM used
Fit status
Runs well
Decode
25.5 tok/s
TTFT
7592 ms
Safe context
200K
Memory
10.9 GB / 23.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsglm 4 9b chat 1m on MacBook Pro M2 Pro 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: 25.5 tok/s decode · 7.6s TTFT (warm) · 64 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 | 25.5 tok/s | 4141 ms | 200K |
| Coding | C | Runs well | 25.5 tok/s | 7592 ms | 200K |
| Agentic Coding | C | Runs well | 25.5 tok/s | 11043 ms | 200K |
| Reasoning | C | Runs well | 25.5 tok/s | 8972 ms | 200K |
| RAG | C | Runs well | 25.5 tok/s | 13803 ms | 200K |
Quantization options
How glm 4 9b chat 1m (9B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
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 startOpções de upgrade
Hardware que roda bem glm 4 9b chat 1m
MacBook Pro M4 Max 48GBMelhor custo-benefício
48 GB Unified (+16)546 GB/s (+346)
C
Raises estimated decode speed by about 168%.68.3 tok/s decodificação
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBUpgrade Apple
64 GB Unified (+32)800 GB/s (+600)
C
Raises estimated decode speed by about 231%.84.5 tok/s decodificação
Raises estimated decode speed by about 231%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Frequently asked questions
Can MacBook Pro M2 Pro 32GB run glm 4 9b chat 1m?
Yes, MacBook Pro M2 Pro 32GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 25.5 tok/s.
How much VRAM does glm 4 9b chat 1m need?
glm 4 9b chat 1m (9B parameters) requires approximately 10.9 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 Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, glm 4 9b chat 1m achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7592ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 32GB run glm 4 9b chat 1m for coding?
For coding workloads, glm 4 9b chat 1m on MacBook Pro M2 Pro 32GB receives a C grade with 25.5 tok/s and 200K context.
What context window can glm 4 9b chat 1m use on MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, glm 4 9b chat 1m can safely use up to 200K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for glm 4 9b chat 1m?
Not always. MacBook Pro M2 Pro 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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