Can GLM-4 9B run on Radeon Pro W7500 8GB?

YES — With Offload

A71Great

Estimated from fit model

GLM-4 9B needs ~7.8 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.8 GB, 26.3 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

26.3 tok/s

TTFT

7353 ms

Safe context

21K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGLM-4 9B on Radeon Pro W7500 8GB
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: 26.3 tok/s decode · 7.4s TTFT (warm) · 66 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	26.3 tok/s	4011 ms	21K
Coding	A	Runs with offload	26.3 tok/s	7353 ms	21K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	17.8 tok/s	15845 ms	21K
Reasoning	A	Runs with offload	26.3 tok/s	8690 ms	21K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	17.8 tok/s	19806 ms

Inference speed

GLM-4 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GLM-4 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A75
Q3_K_S	3	4.4 GB	Low	A75
NVFP4Best for your GPU

Get started

Copy-paste commands to run GLM-4 9B on your machine.

Run

ollama run glm4

Frequently asked questions

Can Radeon Pro W7500 8GB run GLM-4 9B?

Yes, Radeon Pro W7500 8GB can run GLM-4 9B with a A grade (Runs with offload). Expected decode speed: 26.3 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 7.8 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 Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, GLM-4 9B achieves approximately 26.3 tokens per second decode speed with a time-to-first-token of 7353ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on Radeon Pro W7500 8GB receives a A grade with 26.3 tok/s and 21K context.

What context window can GLM-4 9B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, GLM-4 9B can safely use up to 21K 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 Radeon Pro W7500 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon Pro W7500 8GB See all hardware for GLM-4 9B

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