Will It Run AI

Can aya expanse 32b heretic MPOA i1 run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C46Usable
Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~34.5 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
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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) 34.5 GB, 11.9 tok/s, Runs well
34.5 GB required69.1 GB available
50% VRAM used

Fit status

Runs well

Decode

11.9 tok/s

TTFT

16289 ms

Safe context

164K

Memory

34.5 GB / 69.1 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsaya expanse 32b heretic MPOA i1 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: 11.9 tok/s decode · 16.3s TTFT (warm) · 30 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 well11.9 tok/s8885 ms164K
CodingCRuns well11.9 tok/s16289 ms164K
Agentic CodingCRuns well11.9 tok/s23693 ms164K
ReasoningCRuns well11.9 tok/s19251 ms164K
RAGCRuns well11.9 tok/s29617 ms164K

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC41
Q3_K_S
3
15.7 GB
LowC42
NVFP4
4
17.9 GB
MediumC42
Q4_K_M
4
19.5 GB
MediumC43
Q5_K_M
5
23.0 GB
HighC43
Q6_K
6
26.2 GB
HighC44
Q8_0Best for your GPU
8
34.2 GB
Very HighC46
F16
16
65.6 GB
MaximumF0

Get started

Copy-paste commands to run aya expanse 32b heretic MPOA i1 on your machine.

Run

lms load hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf && lms server start

升级选项

能流畅运行 aya expanse 32b heretic MPOA i1 的硬件

Frequently asked questions

Can MacBook Pro M2 Max 96GB run aya expanse 32b heretic MPOA i1?

Yes, MacBook Pro M2 Max 96GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 11.9 tok/s.

How much VRAM does aya expanse 32b heretic MPOA i1 need?

aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 34.5 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 32b heretic MPOA i1?

The recommended quantization for aya expanse 32b heretic MPOA i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will aya expanse 32b heretic MPOA i1 run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, aya expanse 32b heretic MPOA i1 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 heretic MPOA i1 for coding?

For coding workloads, aya expanse 32b heretic MPOA i1 on MacBook Pro M2 Max 96GB receives a C grade with 11.9 tok/s and 164K context.

What context window can aya expanse 32b heretic MPOA i1 use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, aya expanse 32b heretic MPOA i1 can safely use up to 164K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for aya expanse 32b heretic MPOA i1?

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.

See all results for MacBook Pro M2 Max 96GBSee all hardware for aya expanse 32b heretic MPOA i1
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