Will It Run AI

Can aya expanse 8b orthogonal heretic run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C45Usable
Estimated from fit model

aya expanse 8b orthogonal heretic needs ~34.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~112 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) — 34.4 GB, 112.0 tok/s, Runs well
34.4 GB required184.3 GB available
19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

2.6M

Memory

34.4 GB / 184.3 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

See how fast it feelsaya expanse 8b orthogonal heretic 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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 well112.0 tok/s943 ms2.6M
CodingCRuns well112.0 tok/s1729 ms2.6M
Agentic CodingCRuns well112.0 tok/s2514 ms2.6M
ReasoningCRuns well112.0 tok/s2043 ms2.6M
RAGCRuns well112.0 tok/s3143 ms2.6M

Quantization options

How aya expanse 8b orthogonal heretic (8B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowD37
Q3_K_S
3
3.9 GB
LowD37
NVFP4
4
4.5 GB
MediumD37
Q4_K_M
4
4.9 GB
MediumD37
Q5_K_M
5
5.8 GB
HighD37
Q6_K
6
6.6 GB
HighD37
Q8_0
8
8.6 GB
Very HighD37
F16Best for your GPU
16
16.4 GB
MaximumD37

Get started

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

Run

lms load hf-mradermacher--aya-expanse-8b-orthogonal-heretic-gguf && lms server start

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run aya expanse 8b orthogonal heretic?

Yes, Mac Studio M3 Ultra 256GB can run aya expanse 8b orthogonal heretic with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does aya expanse 8b orthogonal heretic need?

aya expanse 8b orthogonal heretic (8B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 8b orthogonal heretic?

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

What speed will aya expanse 8b orthogonal heretic run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, aya expanse 8b orthogonal heretic achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run aya expanse 8b orthogonal heretic for coding?

For coding workloads, aya expanse 8b orthogonal heretic on Mac Studio M3 Ultra 256GB receives a C grade with 112.0 tok/s and 2.6M context.

What context window can aya expanse 8b orthogonal heretic use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, aya expanse 8b orthogonal heretic can safely use up to 2.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for aya expanse 8b orthogonal heretic?

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 8b orthogonal heretic
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