Can Gemma 2 27B run on MacBook Pro M4 Max 64GB?

YES — Runs Great

A72Great

Estimated from fit model

Gemma 2 27B needs ~35.5 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 35.5 GB, 26.6 tok/s, Runs well

35.5 GB required46.1 GB available

77% VRAM used

Fit status

Runs well

Decode

26.6 tok/s

TTFT

7278 ms

Safe context

Memory

35.5 GB / 46.1 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGemma 2 27B on MacBook Pro M4 Max 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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	A	Runs well	26.6 tok/s	3970 ms	8K
Coding	A	Runs well	26.6 tok/s	7278 ms	8K
Agentic Coding	B	Runs with offload (needs ~0.2 GB host RAM)	25.7 tok/s	10950 ms	8K
Reasoning	A	Runs well	26.6 tok/s	8602 ms	8K
RAG	B	Runs with offload (needs ~0.2 GB host RAM)	25.7 tok/s	13688 ms	8K

Inference speed

Gemma 2 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 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	58.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	26.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	26.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	26.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	20.9	Too big
RTX 3090 24GB	24 GB	Q4_K_M	17.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	16.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	12.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	11.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	10.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B63
Q3_K_S	3	13.2 GB	Low	B64
NVFP4	4	15.1 GB	Medium	B64
Q4_K_M	4	16.5 GB	Medium	B65
Q5_K_M	5	19.4 GB	High	B66
Q6_K	6	22.1 GB	High	B67
Q8_0Best for your GPU	8	28.9 GB	Very High	B68
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 2 27B on your machine.

Run

ollama run gemma2:27b

Your hardware

More models your MacBook Pro M4 Max 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
Qwen 3.6 35B A3B	35B	S	43.7 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.8 tok/s
Qwen 3.5 35B A3B	35B	S	47.5 tok/s
Qwen 3 32B	32B	S	33.5 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Gemma 2 27B?

Yes, MacBook Pro M4 Max 64GB can run Gemma 2 27B with a A grade (Runs well). Expected decode speed: 26.6 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 35.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 27B?

The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 27B run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Gemma 2 27B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7278ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on MacBook Pro M4 Max 64GB receives a A grade with 26.6 tok/s and 8K context.

What context window can Gemma 2 27B use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Gemma 2 27B?

Not always. MacBook Pro M4 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 M4 Max 64GB See all hardware for Gemma 2 27B

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