Can Aya Expanse 32B run on MacBook Pro M2 Max 96GB?

Q: Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Aya Expanse 32B?

Not always. MacBook Pro M2 Max 96GB 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

C51Usable

Estimated from fit model

Aya Expanse 32B needs ~33.2 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~12 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) — 33.2 GB, 12.9 tok/s, Runs well

33.2 GB required69.1 GB available

48% VRAM used

Fit status

Runs well

Decode

12.9 tok/s

TTFT

14978 ms

Safe context

Memory

33.2 GB / 69.1 GB

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on MacBook Pro M2 Max 96GB
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: 12.9 tok/s decode · 15.0s TTFT (warm) · 32 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	C	Runs well	12.9 tok/s	8170 ms	8K
Coding	C	Runs well	11.9 tok/s	16289 ms	8K
Agentic Coding	C	Runs well	12.9 tok/s	21787 ms	8K
Reasoning	C	Runs well	12.9 tok/s	17702 ms	8K
RAG	C	Runs well	12.9 tok/s	27234 ms	8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C47
Q3_K_S	3	15.7 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Aya Expanse 32B on your machine.

Run

ollama run aya-expanse:32b

Upgrade options

Hardware that runs Aya Expanse 32B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 101%.25.9 tok/s decode

Raises estimated decode speed by about 101%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 90%.24.5 tok/s decode

Raises estimated decode speed by about 90%.

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

~$3,999 MSRP

MacBook Pro M4 Max 128GBApple upgrade

128 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 160%.33.5 tok/s decode

Raises estimated decode speed by about 160%.

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

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Aya Expanse 32B?

Yes, MacBook Pro M2 Max 96GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 11.9 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 33.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 32B?

The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 32B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Aya Expanse 32B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16289ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on MacBook Pro M2 Max 96GB receives a C grade with 11.9 tok/s and 8K context.

What context window can Aya Expanse 32B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Aya Expanse 32B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Aya Expanse 32B?

See all results for MacBook Pro M2 Max 96GB See all hardware for Aya Expanse 32B

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