Is unified memory on Mac mini M4 64GB as fast as VRAM for DeepSeek V4 Pro?

Not always. Mac mini M4 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.

Can DeepSeek V4 Pro run on Mac mini M4 64GB?

Q: What should I upgrade first if DeepSeek V4 Pro feels slow on Mac mini M4 64GB?

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

DeepSeek V4 Pro needs ~871.7 GB but Mac mini M4 64GB only has 46.1 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

NVFP4 (Medium quality) — 871.7 GB, exceeds 46.1 GB available

871.7 GB required46.1 GB available

1891% VRAM needed

825.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

871.7 GB / 46.1 GB

Offload

90%

Memory breakdown

Weights862.0 GB

KV Cache1.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Pro on Mac mini M4 64GB
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 871.7 GB, but this setup only exposes 46.1 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

Inference speed

DeepSeek V4 Pro inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek V4 Pro at NVFP4 across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 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	NVFP4	2.0	Too big
RTX 4090 24GB	24 GB	NVFP4	2.0	Too big
RTX 4080 Super 16GB	16 GB	NVFP4	2.0

Quantization options

How DeepSeek V4 Pro (1600B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	624.0 GB	Low	F0
Q3_K_S	3	784.0 GB	Low	F0
NVFP4	4

Frequently asked questions

Can Mac mini M4 64GB run DeepSeek V4 Pro?

No, DeepSeek V4 Pro requires more memory than Mac mini M4 64GB provides.

How much VRAM does DeepSeek V4 Pro need?

DeepSeek V4 Pro (1600B parameters) requires approximately 871.7 GB of memory with NVFP4 quantization.

What is the best quantization for DeepSeek V4 Pro?

The recommended quantization for DeepSeek V4 Pro is NVFP4, which balances quality and memory efficiency.

What speed will DeepSeek V4 Pro run at on Mac mini M4 64GB?

On Mac mini M4 64GB, DeepSeek V4 Pro achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using NVFP4 quantization.

Can Mac mini M4 64GB run DeepSeek V4 Pro for coding?

For coding workloads, DeepSeek V4 Pro on Mac mini M4 64GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek V4 Pro use on Mac mini M4 64GB?

On Mac mini M4 64GB, DeepSeek V4 Pro can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V4 Pro feels slow on Mac mini M4 64GB?

See all results for Mac mini M4 64GB See all hardware for DeepSeek V4 Pro

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