Mac Studio M3 Ultra 96GBBudget pick
96 GB Unified (+48)819 GB/s (+51)
B
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
Can GLM-4 9B run on RTX A6000 48GB?
YES — Runs Great
B69Good
○Estimated from fit model
GLM-4 9B needs ~12.1 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~106 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 12.1 GB, 116.3 tok/s, Runs well
12.1 GB required48.0 GB available
25% VRAM used
Fit status
Runs well
Decode
116.3 tok/s
TTFT
1665 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 RTX A6000 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: 116.3 tok/s decode · 1.7s TTFT (warm) · 291 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 | 106.3 tok/s | 993 ms | 128K |
| Coding | B | Runs well | 106.3 tok/s | 1821 ms | 128K |
| Agentic Coding | B | Runs well | 106.3 tok/s | 2649 ms | 128K |
| Reasoning | B | Runs well | 106.3 tok/s | 2152 ms | 128K |
| RAG | B | Runs well | 106.3 tok/s | 3311 ms | 128K |
Quantization options
How GLM-4 9B (9B params) fits at each quantization level on RTX A6000 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 |
Get started
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Upgrade options