Can Gemma 4 31B run on MacBook Pro M4 Max 48GB?

BARELY — Tight on Memory

A76Great

Estimated from fit model

Gemma 4 31B needs ~39.5 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 39.5 GB, 20.7 tok/s, Very compromised (needs ~2.3 GB host RAM)

39.5 GB required34.6 GB available

114% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.3 GB host RAM)

Decode

20.7 tok/s

TTFT

9348 ms

Safe context

11K

Memory

39.5 GB / 34.6 GB

Offload

10%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsGemma 4 31B on MacBook Pro M4 Max 48GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 20.7 tok/s decode · 9.3s TTFT (warm) · 52 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 2.3 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	S	Tight fit	25.5 tok/s	4138 ms	11K
Coding	A	Very compromised (needs ~2.3 GB host RAM)	20.7 tok/s	9348 ms	11K
Agentic Coding	F	Too heavy	14.2 tok/s	19805 ms	11K
Reasoning	A	Very compromised (needs ~2.3 GB host RAM)	20.7 tok/s	11047 ms	11K
RAG	F	Too heavy	14.2 tok/s	24757 ms	11K

Inference speed

Gemma 4 31B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 31B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~26 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	25.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	25.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	23.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	19.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	18.7	Fits
RTX 5090 32GB	32 GB	Q4_K_M	15.0	Heavy offload
RTX 4090 24GB	24 GB	Q4_K_M	13.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	13.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	11.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	10.2	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	9.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.8	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	5.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	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 4 31B (30.700000762939453B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A83
Q3_K_S	3	15.0 GB	Low	A85
NVFP4	4	17.2 GB	Medium	S86
Q4_K_M	4	18.7 GB	Medium	S86
Q5_K_M	5	22.1 GB	High	S86
Q6_KBest for your GPU	6	25.2 GB	High	S85
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your MacBook Pro M4 Max 48GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	43.7 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 48GB run Gemma 4 31B?

Yes, MacBook Pro M4 Max 48GB can run Gemma 4 31B with a A grade (Very compromised (needs ~2.3 GB host RAM)). Expected decode speed: 20.7 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 39.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Gemma 4 31B achieves approximately 20.7 tokens per second decode speed with a time-to-first-token of 9348ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 48GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on MacBook Pro M4 Max 48GB receives a A grade with 20.7 tok/s and 11K context.

What context window can Gemma 4 31B use on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Gemma 4 31B can safely use up to 11K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on MacBook Pro M4 Max 48GB?

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.

Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for Gemma 4 31B?

Not always. MacBook Pro M4 Max 48GB 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 48GB See all hardware for Gemma 4 31B

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