Will It Run AI

Can Aya Expanse 32B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C50Usable
Estimated from fit model

Aya Expanse 32B needs ~50.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 50.5 GB, 31.0 tok/s, Runs well
50.5 GB required184.3 GB available
27% VRAM used

Fit status

Runs well

Decode

31.0 tok/s

TTFT

6240 ms

Safe context

8K

Memory

50.5 GB / 184.3 GB

Memory breakdown

Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on Mac Studio M3 Ultra 256GB
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: 31.0 tok/s decode · 6.2s TTFT (warm) · 78 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well31.0 tok/s3403 ms8K
CodingCRuns well31.0 tok/s6240 ms8K
Agentic CodingCRuns well31.0 tok/s9076 ms8K
ReasoningCRuns well31.0 tok/s7374 ms8K
RAGCRuns well31.0 tok/s11345 ms8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC43
Q3_K_S
3
15.7 GB
LowC43
NVFP4
4
17.9 GB
MediumC43
Q4_K_M
4
19.5 GB
MediumC43
Q5_K_M
5
23.0 GB
HighC43
Q6_K
6
26.2 GB
HighC44
Q8_0
8
34.2 GB
Very HighC45
F16Best for your GPU
16
65.6 GB
MaximumC48

Get started

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

Run

ollama run aya-expanse:32b

Opciones de mejora

Hardware que ejecuta bien Aya Expanse 32B

AMD Instinct MI350X 288GBOpción económica
288 GB VRAM (+32)8000 GB/s (+7181)
C
Sube la velocidad estimada de decodificación alrededor de un 950%.325.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 950%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$8,000 MSRP

AMD Instinct MI300X 192GBMejor relación calidad-precio
5300 GB/s (+4481)
C
Sube la velocidad estimada de decodificación alrededor de un 643%.230.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 643%.

~$15,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Aya Expanse 32B?

Yes, Mac Studio M3 Ultra 256GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 31.0 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 50.5 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 Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Aya Expanse 32B achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6240ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on Mac Studio M3 Ultra 256GB receives a C grade with 31.0 tok/s and 8K context.

What context window can Aya Expanse 32B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, 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 Mac Studio M3 Ultra 256GB as fast as VRAM for Aya Expanse 32B?

Not always. Mac Studio M3 Ultra 256GB 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.

See all results for Mac Studio M3 Ultra 256GBSee all hardware for Aya Expanse 32B
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