RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+992)
B
Raises estimated decode speed by about 211%.182 tok/s decodificação
Raises estimated decode speed by about 211%.
~$9,999 MSRP
Can Vicuna 13B run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
B69Good
Estimated from fit model
Vicuna 13B needs ~34.9 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~59 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 34.9 GB, 58.5 tok/s, Runs well
34.9 GB required92.2 GB available
38% VRAM used
Fit status
Runs well
Decode
58.5 tok/s
TTFT
3309 ms
Safe context
4K
Memory
34.9 GB / 92.2 GB
Memory breakdown
Weights7.9 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsVicuna 13B on Mac Studio M2 Ultra 128GB
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: 58.5 tok/s decode · 3.3s TTFT (warm) · 146 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 58.5 tok/s | 1805 ms | 4K |
| Coding | B | Runs well | 58.5 tok/s | 3309 ms | 4K |
| Agentic Coding | A | Runs well | 58.5 tok/s | 4813 ms | 4K |
| Reasoning | B | Runs well | 58.5 tok/s | 3910 ms | 4K |
| RAG | A | Runs well | 58.5 tok/s | 6016 ms | 4K |
Quantization options
How Vicuna 13B (13B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B60 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
NVFP4 | 4 | 7.3 GB | Medium | B60 |
Q4_K_M | 4 | 7.9 GB | Medium | B60 |
Q5_K_M | 5 | 9.4 GB | High | B60 |
Q6_K | 6 | 10.7 GB | High | B60 |
Q8_0 | 8 | 13.9 GB | Very High | B61 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B62 |
Get started
Copy-paste commands to run Vicuna 13B on your machine.
Run
ollama run vicuna:13bOpções de upgrade
Hardware que roda bem Vicuna 13B
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run Vicuna 13B?
Yes, Mac Studio M2 Ultra 128GB can run Vicuna 13B with a B grade (Runs well). Expected decode speed: 58.5 tok/s.
How much VRAM does Vicuna 13B need?
Vicuna 13B (13B parameters) requires approximately 34.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Vicuna 13B?
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
What speed will Vicuna 13B run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Vicuna 13B achieves approximately 58.5 tokens per second decode speed with a time-to-first-token of 3309ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run Vicuna 13B for coding?
For coding workloads, Vicuna 13B on Mac Studio M2 Ultra 128GB receives a B grade with 58.5 tok/s and 4K context.
What context window can Vicuna 13B use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Vicuna 13B?
Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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