Can Nous Dolphin 13B run on MacBook Pro M3 Max 64GB?

YES — Runs Great

A72Great

Estimated from fit model

Nous Dolphin 13B needs ~29.7 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q5_K_M quantization, expect ~26 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 29.7 GB, 26.2 tok/s, Runs well

29.7 GB required46.1 GB available

64% VRAM used

Fit status

Runs well

Decode

26.2 tok/s

TTFT

7402 ms

Safe context

16K

Memory

29.7 GB / 46.1 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsNous Dolphin 13B on MacBook Pro M3 Max 64GB
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: 26.2 tok/s decode · 7.4s TTFT (warm) · 65 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	26.2 tok/s	4038 ms	16K
Coding	A	Runs well	26.2 tok/s	7402 ms	16K
Agentic Coding	A	Tight fit	26.2 tok/s	10767 ms	16K
Reasoning	A	Runs well	26.2 tok/s	8748 ms	16K
RAG	A	Tight fit	26.2 tok/s	13459 ms	16K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B63
Q3_K_S	3	6.4 GB	Low	B63
NVFP4	4	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B64
Q8_0	8	13.9 GB	Very High	B65
F16Best for your GPU	16	26.7 GB	Maximum	B69

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 MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	36.3 tok/s
Qwen 3.5 27B	27B	S	15.7 tok/s
Qwen 3.6 27B	27B	S	15.8 tok/s
Qwen 3.6 35B A3B	35B	S	33.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	37.5 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 64GB run Nous Dolphin 13B?

Yes, MacBook Pro M3 Max 64GB can run Nous Dolphin 13B with a A grade (Runs well). Expected decode speed: 26.2 tok/s.

How much VRAM does Nous Dolphin 13B need?

Nous Dolphin 13B (13B parameters) requires approximately 29.7 GB of memory with Q5_K_M quantization.

What is the best quantization for Nous Dolphin 13B?

The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Nous Dolphin 13B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Nous Dolphin 13B achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7402ms using Q5_K_M quantization.

Can MacBook Pro M3 Max 64GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on MacBook Pro M3 Max 64GB receives a A grade with 26.2 tok/s and 16K context.

What context window can Nous Dolphin 13B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Nous Dolphin 13B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Nous Dolphin 13B?

Not always. MacBook Pro M3 Max 64GB 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 M3 Max 64GB See all hardware for Nous Dolphin 13B

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