Can Vicuna 13B run on MacBook Pro M3 Max 128GB?

Q: Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for Vicuna 13B?

Not always. MacBook Pro M3 Max 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.

YES — Runs Great

B67Good

Estimated from fit model

Vicuna 13B needs ~34.9 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 30.3 tok/s, Runs well

34.9 GB required92.2 GB available

38% VRAM used

Fit status

Runs well

Decode

30.3 tok/s

TTFT

6397 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 MacBook Pro M3 Max 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: 30.3 tok/s decode · 6.4s TTFT (warm) · 76 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	30.3 tok/s	3489 ms	4K
Coding	B	Runs well	30.3 tok/s	6397 ms	4K
Agentic Coding	B	Runs well	30.3 tok/s	9305 ms	4K
Reasoning	B	Runs well	30.3 tok/s	7560 ms	4K
RAG	B	Runs well	30.3 tok/s	11631 ms	4K

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on MacBook Pro M3 Max 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

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

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 132%.70.2 tok/s decode

Raises estimated decode speed by about 132%.

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBBest value

1792 GB/s (+1392)

Raises estimated decode speed by about 501%.182 tok/s decode

Raises estimated decode speed by about 501%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run Vicuna 13B?

Yes, MacBook Pro M3 Max 128GB can run Vicuna 13B with a B grade (Runs well). Expected decode speed: 30.3 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 MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, Vicuna 13B achieves approximately 30.3 tokens per second decode speed with a time-to-first-token of 6397ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on MacBook Pro M3 Max 128GB receives a B grade with 30.3 tok/s and 4K context.

What context window can Vicuna 13B use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 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 MacBook Pro M3 Max 128GB as fast as VRAM for Vicuna 13B?

See all results for MacBook Pro M3 Max 128GB See all hardware for Vicuna 13B

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