Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Llama 3.1 405B?

Not always. MacBook Pro M2 Pro 32GB 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 Llama 3.1 405B run on MacBook Pro M2 Pro 32GB?

Q: What should I upgrade first if Llama 3.1 405B feels slow on MacBook Pro M2 Pro 32GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

NO — Won't Fit

F0Won't run

Estimated from fit model

Llama 3.1 405B needs ~259.1 GB but MacBook Pro M2 Pro 32GB only has 23.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 259.1 GB, exceeds 23.0 GB available

259.1 GB required23.0 GB available

1127% VRAM needed

236.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

259.1 GB / 23.0 GB

Offload

90%

Memory breakdown

Weights247.1 GB

KV Cache7.7 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.1 405B on MacBook Pro M2 Pro 32GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 259.1 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Llama 3.1 405B (405B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	158.0 GB	Low	F0
Q3_K_S	3	198.5 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Llama 3.1 405B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+256)8000 GB/s (+7800)

Makes the model fit on the accelerator instead of staying completely out of reach.25.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$8,000 MSRP

AMD Instinct MI325X 256GBBest value

256 GB VRAM (+224)6000 GB/s (+5800)

Makes the model fit on the accelerator instead of staying completely out of reach.11.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 495%.

~$20,000 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Llama 3.1 405B?

No, Llama 3.1 405B requires more memory than MacBook Pro M2 Pro 32GB provides.

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 259.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 405B?

The recommended quantization for Llama 3.1 405B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 405B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Llama 3.1 405B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on MacBook Pro M2 Pro 32GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Llama 3.1 405B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Llama 3.1 405B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 405B feels slow on MacBook Pro M2 Pro 32GB?

See all results for MacBook Pro M2 Pro 32GB See all hardware for Llama 3.1 405B

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