Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Gemma 2 9B?

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.

Can Gemma 2 9B run on MacBook Pro M4 16GB?

Q: What should I upgrade first if Gemma 2 9B feels slow on MacBook Pro M4 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

C51Usable

Estimated — low-sample bucket· few comparable runs

Gemma 2 9B needs ~13.2 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 13.2 GB, 9.3 tok/s, Very compromised (needs ~0.7 GB host RAM)

13.2 GB required11.5 GB available

115% VRAM needed

1.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

9.3 tok/s

TTFT

20894 ms

Safe context

Memory

13.2 GB / 11.5 GB

Offload

10%

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGemma 2 9B 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: 9.3 tok/s decode · 20.9s TTFT (warm) · 23 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	11.5 tok/s	9166 ms	8K
Coding	C	Very compromised (needs ~0.7 GB host RAM)	9.3 tok/s	20894 ms	8K
Agentic Coding	F	Too heavy	6.3 tok/s	44788 ms	8K
Reasoning	C	Very compromised (needs ~0.7 GB host RAM)	9.3 tok/s	24693 ms	8K
RAG	F	Too heavy	6.3 tok/s	55985 ms	8K

Inference speed

Gemma 2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	125.3

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B65
Q3_K_S	3	4.4 GB	Low	B66
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.11.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$799 MSRP

MacBook Air M4 24GBBest value

24 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.11.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

MacBook Pro M3 24GBApple upgrade

24 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

RTX 5080 Laptop 16GBBiggest leap

768 GB/s (+648)

Removes host-memory offload, which is usually the single biggest latency and throughput win.93.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 905%.

Frequently asked questions

Can MacBook Pro M4 16GB run Gemma 2 9B?

Yes, MacBook Pro M4 16GB can run Gemma 2 9B with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 9.3 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, Gemma 2 9B achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20894ms using Q4_K_M quantization.

Can MacBook Pro M4 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on MacBook Pro M4 16GB receives a C grade with 9.3 tok/s and 8K context.

What context window can Gemma 2 9B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 9B feels slow on MacBook Pro M4 16GB?

See all results for MacBook Pro M4 16GB See all hardware for Gemma 2 9B

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