Can DeepSeek R1 Distill 7B run on Mac mini M4 32GB?

Q: Is unified memory on Mac mini M4 32GB as fast as VRAM for DeepSeek R1 Distill 7B?

Not always. Mac mini M4 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.

YES — Runs Great

B63Good

Estimated — low-sample bucket· few comparable runs

DeepSeek R1 Distill 7B needs ~9.5 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 9.5 GB, 20.2 tok/s, Runs well

9.5 GB required23.0 GB available

41% VRAM used

Fit status

Runs well

Decode

20.2 tok/s

TTFT

9579 ms

Safe context

33K

Memory

9.5 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on Mac mini M4 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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 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	B	Runs well	20.2 tok/s	5225 ms	33K
Coding	B	Runs well	20.2 tok/s	9579 ms	33K
Agentic Coding	B	Runs well	20.2 tok/s	13933 ms	33K
Reasoning	B	Runs well	20.2 tok/s	11321 ms	33K
RAG	B	Runs well	20.2 tok/s	17416 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B62
Q3_K_S	3	3.4 GB	Low	B62
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.

Run

ollama run deepseek-r1:7b

Upgrade options

Hardware that runs DeepSeek R1 Distill 7B well

MacBook Pro M4 Pro 64GBBudget pick

64 GB Unified (+32)273 GB/s (+153)

Raises estimated decode speed by about 144%.49.2 tok/s decode

Raises estimated decode speed by about 144%.

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

~$1,599 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 38%.27.8 tok/s decode

Raises estimated decode speed by about 38%.

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Raises estimated decode speed by about 385%.98 tok/s decode

Raises estimated decode speed by about 385%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run DeepSeek R1 Distill 7B?

Yes, Mac mini M4 32GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 20.2 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, DeepSeek R1 Distill 7B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9579ms using Q4_K_M quantization.

Can Mac mini M4 32GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on Mac mini M4 32GB receives a B grade with 20.2 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on Mac mini M4 32GB?

On Mac mini M4 32GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 32GB as fast as VRAM for DeepSeek R1 Distill 7B?

See all results for Mac mini M4 32GB See all hardware for DeepSeek R1 Distill 7B

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