Can GLM-4 9B run on Radeon Pro W7800 32GB?

YES — Runs Great

B69Good

Estimated from fit model

GLM-4 9B needs ~10.2 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.2 GB, 67.7 tok/s, Runs well

10.2 GB required32.0 GB available

32% VRAM used

Fit status

Runs well

Decode

67.7 tok/s

TTFT

2859 ms

Safe context

128K

Memory

10.2 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGLM-4 9B on Radeon Pro W7800 32GB
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: 67.7 tok/s decode · 2.9s TTFT (warm) · 169 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	67.7 tok/s	1560 ms	128K
Coding	B	Runs well	67.7 tok/s	2859 ms	128K
Agentic Coding	B	Runs well	67.7 tok/s	4159 ms	128K
Reasoning	B	Runs well	67.7 tok/s	3379 ms	128K
RAG	B	Runs well	67.7 tok/s	5199 ms	128K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B65
Q3_K_S	3	4.4 GB	Low	B65
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

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.74.7 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+224)

Raises estimated decode speed by about 36%.92.4 tok/s decode

Raises estimated decode speed by about 36%.

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

~$3,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run GLM-4 9B?

Yes, Radeon Pro W7800 32GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 67.7 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 10.2 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 Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, GLM-4 9B achieves approximately 67.7 tokens per second decode speed with a time-to-first-token of 2859ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on Radeon Pro W7800 32GB receives a B grade with 67.7 tok/s and 128K context.

What context window can GLM-4 9B use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, 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 Radeon Pro W7800 32GB See all hardware for GLM-4 9B

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