Can DeepSeek R1 1.5B run on MacBook Pro M2 Max 32GB?

Q: Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for DeepSeek R1 1.5B?

Not always. MacBook Pro M2 Max 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

C53Usable

Estimated from fit model

DeepSeek R1 1.5B needs ~5.7 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 5.7 GB, 21.0 tok/s, Runs well

5.7 GB required23.0 GB available

25% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

33K

Memory

5.7 GB / 23.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDeepSeek R1 1.5B on MacBook Pro M2 Max 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	C	Runs well	21.0 tok/s	5029 ms	33K
Coding	C	Runs well	21.0 tok/s	9219 ms	33K
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	33K
Reasoning	C	Runs well	21.0 tok/s	10895 ms	33K
RAG	C	Runs well	21.0 tok/s	16762 ms	33K

Quantization options

How DeepSeek R1 1.5B (1.5B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C54
Q3_K_S	3	0.7 GB	Low	C55
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 1.5B on your machine.

Run

ollama run deepseek-r1:1.5b

Frequently asked questions

Can MacBook Pro M2 Max 32GB run DeepSeek R1 1.5B?

Yes, MacBook Pro M2 Max 32GB can run DeepSeek R1 1.5B with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does DeepSeek R1 1.5B need?

DeepSeek R1 1.5B (1.5B parameters) requires approximately 5.7 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 1.5B?

The recommended quantization for DeepSeek R1 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 1.5B run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, DeepSeek R1 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on MacBook Pro M2 Max 32GB receives a C grade with 21.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, DeepSeek R1 1.5B 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 MacBook Pro M2 Max 32GB as fast as VRAM for DeepSeek R1 1.5B?

See all results for MacBook Pro M2 Max 32GB See all hardware for DeepSeek R1 1.5B

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