MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
A
This setup is broadly balanced for this model.74.7 tok/s Dekodierung
ca. $2,499 MSRP
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.
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 | 5.0 GB | Medium | B65 |
Q4_K_M | 4 | 5.5 GB | Medium | B65 |
Q5_K_M | 5 | 6.5 GB | High | B66 |
Q6_K | 6 | 7.4 GB | High | B66 |
Q8_0 | 8 | 9.6 GB | Very High | B67 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | A71 |
Get started
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Upgrade-Optionen
Hardware, die GLM-4 9B gut ausführt
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.
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