Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for GLM-4 9B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can GLM-4 9B run on Intel Data Center GPU Max 1550 128GB?

Q: What should I upgrade first if GLM-4 9B feels slow on Intel Data Center GPU Max 1550 128GB?

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.

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

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) — 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 glm4

Upgrade options

Hardware that runs GLM-4 9B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)

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

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run GLM-4 9B?

Yes, Intel Data Center GPU Max 1550 128GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 19.8 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 Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, GLM-4 9B achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on Intel Data Center GPU Max 1550 128GB receives a B grade with 126.0 tok/s and 128K context.

What context window can GLM-4 9B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, 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.

What should I upgrade first if GLM-4 9B feels slow on Intel Data Center GPU Max 1550 128GB?

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for GLM-4 9B

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