Can Nous Dolphin 13B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

A71Great

○Estimated from fit model

Nous Dolphin 13B needs ~33.1 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q5_K_M quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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

Q5_K_M (High quality) — 33.1 GB, 60.7 tok/s, Runs well

33.1 GB required69.1 GB available

48% VRAM used

Fit status

Runs well

Decode

60.7 tok/s

TTFT

3190 ms

Safe context

16K

Memory

33.1 GB / 69.1 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsNous Dolphin 13B on Mac Studio M3 Ultra 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: 60.7 tok/s decode · 3.2s TTFT (warm) · 152 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	B	Runs well	60.7 tok/s	1740 ms	16K
Coding	A	Runs well	60.7 tok/s	3190 ms	16K
Agentic Coding	A	Runs well	60.7 tok/s	4640 ms	16K
Reasoning	A	Runs well	60.7 tok/s	3770 ms	16K
RAG	A	Runs well	60.7 tok/s	5800 ms	16K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B61
Q3_K_S	3	6.4 GB	Low	B61
NVFP4	4	7.3 GB	Medium	B61
Q4_K_M	4	7.9 GB	Medium	B61
Q5_K_M	5	9.4 GB	High	B61
Q6_K	6	10.7 GB	High	B62
Q8_0	8	13.9 GB	Very High	B62
F16Best for your GPU	16	26.7 GB	Maximum	B65

Get started

Copy-paste commands to run Nous Dolphin 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "nousresearch/Nous-Dolphin-13B" \
 --hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your Mac Studio M3 Ultra 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	84.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	36.5 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	36.6 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	70.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	87.1 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 96GB See all hardware for Nous Dolphin 13B

URL: https://willitrunai.com/can-run/nous-dolphin-13b-on-m3-ultra-96gb

⇱ Nous Dolphin 13B on Mac Studio M3 Ultra 96GB? YES