Can Dolphin 2.9 8B run on Mac mini M2 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

Dolphin 2.9 8B needs ~10.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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) — 10.3 GB, 14.3 tok/s, Runs well

10.3 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

14.3 tok/s

TTFT

13521 ms

Safe context

33K

Memory

10.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on Mac mini M2 24GB
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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 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	14.3 tok/s	7375 ms	33K
Coding	C	Runs well	14.3 tok/s	13521 ms	33K
Agentic Coding	C	Runs well	14.3 tok/s	19667 ms	33K
Reasoning	C	Runs well	14.3 tok/s	15979 ms	33K
RAG	C	Runs well	14.3 tok/s	24583 ms	33K

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run dolphin-llama3

Opciones de mejora

Hardware que ejecuta bien Dolphin 2.9 8B

MacBook Pro M2 Max 32GBOpción económica

32 GB Unified (+8)400 GB/s (+300)

Sube la velocidad estimada de decodificación alrededor de un 257%.51.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 257%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

MacBook Pro M2 Pro 32GBMejor relación calidad-precio

32 GB Unified (+8)200 GB/s (+100)

Sube la velocidad estimada de decodificación alrededor de un 115%.30.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 115%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

MacBook Pro M1 Max 32GBMejora Apple

32 GB Unified (+8)400 GB/s (+300)

Sube la velocidad estimada de decodificación alrededor de un 239%.48.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 239%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Dolphin 2.9 8B?

Yes, Mac mini M2 24GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 14.3 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 10.3 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 M2 24GB?

On Mac mini M2 24GB, Dolphin 2.9 8B achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13521ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on Mac mini M2 24GB receives a C grade with 14.3 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, 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 M2 24GB as fast as VRAM for Dolphin 2.9 8B?

Not always. Mac mini M2 24GB 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 mini M2 24GB See all hardware for Dolphin 2.9 8B

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