Can GLM-4 9B run on NVIDIA GH200 96GB?

YES — Runs Great

B67Good

Estimated from fit model

GLM-4 9B needs ~16.9 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 16.9 GB, 126.0 tok/s, Runs well

16.9 GB required96.0 GB available

18% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

128K

Memory

16.9 GB / 96.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsGLM-4 9B on NVIDIA GH200 96GB
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

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement 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 NVIDIA GH200 96GB (96.0 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	5.0 GB	Medium	B60
Q4_K_M	4	5.5 GB	Medium	B61
Q5_K_M	5	6.5 GB	High	B61
Q6_K	6	7.4 GB	High	B61
Q8_0	8	9.6 GB	Very High	B61
F16Best for your GPU	16	18.5 GB	Maximum	B62

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 (+160)

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 NVIDIA GH200 96GB run GLM-4 9B?

Yes, NVIDIA GH200 96GB 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 16.9 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 NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, 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 NVIDIA GH200 96GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on NVIDIA GH200 96GB receives a B grade with 126.0 tok/s and 128K context.

What context window can GLM-4 9B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, 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.

See all results for NVIDIA GH200 96GB See all hardware for GLM-4 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

126.0 tok/s

838 ms

128K

Coding

Runs well

126.0 tok/s

1537 ms

128K

Agentic Coding

Runs well

126.0 tok/s

2235 ms

128K

Reasoning

Runs well

126.0 tok/s

1816 ms

128K

RAG

Runs well

126.0 tok/s

2794 ms

128K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B60

Q3_K_S

4.4 GB

Low

B60

NVFP4

5.0 GB

Medium

B60

Q4_K_M

5.5 GB

Medium

B61

Q5_K_M

6.5 GB

High

B61

Q6_K

7.4 GB

High

B61

Q8_0

9.6 GB

Very High

B61

F16Best for your GPU

18.5 GB

Maximum

B62

Frequently asked questions

Can NVIDIA GH200 96GB run GLM-4 9B?

Yes, NVIDIA GH200 96GB 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 16.9 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 NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, 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 NVIDIA GH200 96GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on NVIDIA GH200 96GB receives a B grade with 126.0 tok/s and 128K context.

What context window can GLM-4 9B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, 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.