Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.111 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can GLM-4 9B run on Intel Data Center GPU Max 1550 128GB?
YES — Runs Great
B67Good
○Estimated from fit model
GLM-4 9B needs ~19.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~126 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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.
Select quantization to explore
Q4_K_M (Medium quality) — 19.8 GB, 126.0 tok/s, Runs well
19.8 GB required128.0 GB available
15% VRAM used
Fit status
Runs well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
128K
Memory
19.8 GB / 128.0 GB
Memory breakdown
Weights5.5 GB
KV Cache0.6 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsGLM-4 9B on Intel Data Center GPU Max 1550 128GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 126.0 tok/s | 838 ms | 128K |
| Coding | B | Runs well | 126.0 tok/s | 1537 ms | 128K |
| Agentic Coding | B | Runs well | 126.0 tok/s | 2235 ms | 128K |
| Reasoning | B | Runs well | 126.0 tok/s | 1816 ms | 128K |
| RAG | B | Runs well | 126.0 tok/s | 2794 ms | 128K |
Quantization options
How GLM-4 9B (9B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B60 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 |
Get started
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Upgrade options