Can glm 4 9b chat 1m run on NVIDIA H200 141GB?

YES — Runs Great

C45Usable

Estimated from fit model

glm 4 9b chat 1m needs ~21.8 GB VRAM. NVIDIA H200 141GB has 141.0 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) — 21.8 GB, 126.0 tok/s, Runs well

21.8 GB required141.0 GB available

15% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

1.8M

Memory

21.8 GB / 141.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on NVIDIA H200 141GB
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	C	Runs well	126.0 tok/s	838 ms	1.8M
Coding	C	Runs well	126.0 tok/s	1537 ms	1.8M
Agentic Coding	C	Runs well	126.0 tok/s	2235 ms	1.8M
Reasoning	C	Runs well	126.0 tok/s	1816 ms	1.8M
RAG	C	Runs well	126.0 tok/s	2794 ms	1.8M

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D38
Q3_K_S	3	4.4 GB	Low	D38
NVFP4	4

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 start

Upgrade options

Hardware that runs glm 4 9b chat 1m well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+115)

Adds memory headroom for longer context windows and future model growth.101.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

Frequently asked questions

Can NVIDIA H200 141GB run glm 4 9b chat 1m?

Yes, NVIDIA H200 141GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does glm 4 9b chat 1m need?

glm 4 9b chat 1m (9B parameters) requires approximately 21.8 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 NVIDIA H200 141GB?

On NVIDIA H200 141GB, glm 4 9b chat 1m achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on NVIDIA H200 141GB receives a C grade with 126.0 tok/s and 1.8M context.

What context window can glm 4 9b chat 1m use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, glm 4 9b chat 1m can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H200 141GB See all hardware for glm 4 9b chat 1m

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