Can GLM-4 9B run on NVIDIA A40 48GB?

YES — Runs Great

B69Good

Estimated from fit model

GLM-4 9B needs ~12.1 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~108 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 12.1 GB, 108.2 tok/s, Runs well

12.1 GB required48.0 GB available

25% VRAM used

Fit status

Runs well

Decode

108.2 tok/s

TTFT

1790 ms

Safe context

128K

Memory

12.1 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGLM-4 9B on NVIDIA A40 48GB
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: 108.2 tok/s decode · 1.8s TTFT (warm) · 270 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	108.2 tok/s	976 ms	128K
Coding	B	Runs well	108.2 tok/s	1790 ms	128K
Agentic Coding	B	Runs well	108.2 tok/s	2604 ms	128K
Reasoning	B	Runs well	108.2 tok/s	2115 ms	128K
RAG	B	Runs well	108.2 tok/s	3255 ms	128K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B63
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4	5.0 GB	Medium	B63
Q4_K_M	4	5.5 GB	Medium	B63
Q5_K_M	5	6.5 GB	High	B63
Q6_K	6	7.4 GB	High	B64
Q8_0	8	9.6 GB	Very High	B64
F16Best for your GPU	16	18.5 GB	Maximum	B67

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 96GBBudget pick

96 GB Unified (+48)819 GB/s (+123)

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.

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A40 48GB run GLM-4 9B?

Yes, NVIDIA A40 48GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 108.2 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 12.1 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 A40 48GB?

On NVIDIA A40 48GB, GLM-4 9B achieves approximately 108.2 tokens per second decode speed with a time-to-first-token of 1790ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on NVIDIA A40 48GB receives a B grade with 108.2 tok/s and 128K context.

What context window can GLM-4 9B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, 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 A40 48GB See all hardware for GLM-4 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

108.2 tok/s

976 ms

128K

Coding

Runs well

108.2 tok/s

1790 ms

128K

Agentic Coding

Runs well

108.2 tok/s

2604 ms

128K

Reasoning

Runs well

108.2 tok/s

2115 ms

128K

RAG

Runs well

108.2 tok/s

3255 ms

128K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B63

Q3_K_S

4.4 GB

Low

B63

NVFP4

5.0 GB

Medium

B63

Q4_K_M

5.5 GB

Medium

B63

Q5_K_M

6.5 GB

High

B63

Q6_K

7.4 GB

High

B64

Q8_0

9.6 GB

Very High

B64

F16Best for your GPU

18.5 GB

Maximum

B67

Frequently asked questions

Can NVIDIA A40 48GB run GLM-4 9B?

Yes, NVIDIA A40 48GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 108.2 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 12.1 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 A40 48GB?

On NVIDIA A40 48GB, GLM-4 9B achieves approximately 108.2 tokens per second decode speed with a time-to-first-token of 1790ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on NVIDIA A40 48GB receives a B grade with 108.2 tok/s and 128K context.

What context window can GLM-4 9B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, 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.