Can mxbai Embed Large run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A71Great

○Estimated from fit model

mxbai Embed Large needs ~17.2 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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

F16 (Maximum quality) — 17.2 GB, 4.7 tok/s, Runs well

17.2 GB required92.2 GB available

19% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

17.2 GB / 92.2 GB

Memory breakdown

Weights0.7 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsmxbai Embed Large on Mac Studio M2 Ultra 128GB
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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	4.7 tok/s	22516 ms	512
Coding	A	Runs well	4.7 tok/s	41279 ms	512
Agentic Coding	A	Runs well	4.7 tok/s	60043 ms	512
Reasoning	A	Runs well	4.7 tok/s	48785 ms	512
RAG	A	Runs well	4.7 tok/s	75053 ms	512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A73
Q3_K_S	3	0.2 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run mxbai Embed Large on your machine.

Run

ollama run mxbai-embed-large

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	6.3 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GB See all hardware for mxbai Embed Large

URL: https://willitrunai.com/can-run/mxbai-embed-large-on-m2-ultra-128gb

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