Can Dolphin 2.9 8B run on MacBook Pro M4 16GB?

Q: Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Dolphin 2.9 8B?

Not always. MacBook Pro M4 16GB 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 — Tight Fit

C49Usable

Estimated — low-sample bucket· few comparable runs

Dolphin 2.9 8B needs ~9.5 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 17.5 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

17.5 tok/s

TTFT

11056 ms

Safe context

33K

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on MacBook Pro M4 16GB
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	Tight fit	17.5 tok/s	11056 ms	33K
Agentic Coding	C	Runs with offload	17.5 tok/s	16082 ms	33K
Reasoning	C	Tight fit	17.5 tok/s	13067 ms	33K
RAG	C	Runs with offload	17.5 tok/s	20103 ms	33K

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C52
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 Air M4 24GBBudget pick

24 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.17.5 tok/s decode

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

~$1,099 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.15 tok/s decode

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

~$1,099 MSRP

MacBook Air M3 24GBApple upgrade

24 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.15 tok/s decode

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

~$1,099 MSRP

RTX 3080 Ti 12GBBiggest leap

912 GB/s (+792)

Raises estimated decode speed by about 449%.96 tok/s decode

Raises estimated decode speed by about 449%.

~$1,199 MSRP

Frequently asked questions

Can MacBook Pro M4 16GB run Dolphin 2.9 8B?

Yes, MacBook Pro M4 16GB can run Dolphin 2.9 8B with a C grade (Tight fit). Expected decode speed: 17.5 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 9.5 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 MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, 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 MacBook Pro M4 16GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on MacBook Pro M4 16GB receives a C grade with 17.5 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, 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 MacBook Pro M4 16GB as fast as VRAM for Dolphin 2.9 8B?

See all results for MacBook Pro M4 16GB See all hardware for Dolphin 2.9 8B

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