Can aya expanse 8b run on Mac mini M4 32GB?

Q: Is unified memory on Mac mini M4 32GB as fast as VRAM for aya expanse 8b?

Not always. Mac mini M4 32GB 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

C46Usable

Estimated — low-sample bucket· few comparable runs

aya expanse 8b needs ~10.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 10.2 GB, 16.3 tok/s, Runs well

10.2 GB required23.0 GB available

44% VRAM used

Fit status

Runs well

Decode

16.3 tok/s

TTFT

11886 ms

Safe context

236K

Memory

10.2 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsaya expanse 8b on Mac mini M4 32GB
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: 16.3 tok/s decode · 11.9s TTFT (warm) · 41 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	16.3 tok/s	6483 ms	236K
Coding	C	Runs well	16.3 tok/s	11886 ms	236K
Agentic Coding	C	Runs well	16.3 tok/s	17288 ms	236K
Reasoning	C	Runs well	16.3 tok/s	14047 ms	236K
RAG	C	Runs well	16.3 tok/s	21610 ms	236K

Inference speed

aya expanse 8b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for aya expanse 8b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How aya expanse 8b (8B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run aya expanse 8b on your machine.

Run

lms load hf-bartowski--aya-expanse-8b-gguf && lms server start

Upgrade options

Hardware that runs aya expanse 8b well

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 37%.22.4 tok/s decode

Raises estimated decode speed by about 37%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 254%.57.7 tok/s decode

Raises estimated decode speed by about 254%.

~$2,499 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Raises estimated decode speed by about 483%.95.1 tok/s decode

Raises estimated decode speed by about 483%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run aya expanse 8b?

Yes, Mac mini M4 32GB can run aya expanse 8b with a C grade (Runs well). Expected decode speed: 16.3 tok/s.

How much VRAM does aya expanse 8b need?

aya expanse 8b (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 8b?

The recommended quantization for aya expanse 8b is Q4_K_M, which balances quality and memory efficiency.

What speed will aya expanse 8b run at on Mac mini M4 32GB?

On Mac mini M4 32GB, aya expanse 8b achieves approximately 16.3 tokens per second decode speed with a time-to-first-token of 11886ms using Q4_K_M quantization.

Can Mac mini M4 32GB run aya expanse 8b for coding?

For coding workloads, aya expanse 8b on Mac mini M4 32GB receives a C grade with 16.3 tok/s and 236K context.

What context window can aya expanse 8b use on Mac mini M4 32GB?

On Mac mini M4 32GB, aya expanse 8b can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 32GB as fast as VRAM for aya expanse 8b?

See all results for Mac mini M4 32GB See all hardware for aya expanse 8b

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