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.

Capabilities:

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

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:13b

Upgrade options

Hardware that runs Vicuna 13B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+992)

Raises estimated decode speed by about 211%.182 tok/s decode

Raises estimated decode speed by about 211%.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+797)

Raises estimated decode speed by about 189%.169.2 tok/s decode

Raises estimated decode speed by about 189%.