Can Dolphin 2.9 8B run on Mac mini M4 32GB?

Q: Is unified memory on Mac mini M4 32GB as fast as VRAM for Dolphin 2.9 8B?

Not always. Mac mini M4 32GB 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.

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

Dolphin 2.9 8B needs ~11.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 11.2 GB, 17.5 tok/s, Runs well

11.2 GB required23.0 GB available

49% VRAM used

Fit status

Runs well

Decode

17.5 tok/s

TTFT

11056 ms

Safe context

33K

Memory

11.2 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on Mac mini M4 32GB
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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 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	C	Runs well	17.5 tok/s	6031 ms	33K
Coding	C	Runs well	17.5 tok/s	11056 ms	33K
Agentic Coding	C	Runs well	17.5 tok/s	16082 ms	33K
Reasoning	C	Runs well	17.5 tok/s	13067 ms	33K
RAG	C	Runs well	17.5 tok/s	20103 ms	33K

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run Dolphin 2.9 8B on your machine.

Run

ollama run dolphin-llama3

Upgrade options

Hardware that runs Dolphin 2.9 8B well

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 38%.24.1 tok/s decode

Raises estimated decode speed by about 38%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 254%.62 tok/s decode

Raises estimated decode speed by about 254%.

~$2,499 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Raises estimated decode speed by about 484%.102.2 tok/s decode

Raises estimated decode speed by about 484%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run Dolphin 2.9 8B?

Yes, Mac mini M4 32GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 17.5 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin 2.9 8B?

The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin 2.9 8B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, Dolphin 2.9 8B achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11056ms using Q4_K_M quantization.

Can Mac mini M4 32GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on Mac mini M4 32GB receives a C grade with 17.5 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on Mac mini M4 32GB?

On Mac mini M4 32GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 32GB as fast as VRAM for Dolphin 2.9 8B?

See all results for Mac mini M4 32GB See all hardware for Dolphin 2.9 8B

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