Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for DeepSeek R1 Distill 70B?

Not always. MacBook Pro M3 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.

Can DeepSeek R1 Distill 70B run on MacBook Pro M3 Max 48GB?

Q: What should I upgrade first if DeepSeek R1 Distill 70B feels slow on MacBook Pro M3 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.

YES — With Q2_K

B62Good

Estimated from fit model

DeepSeek R1 Distill 70B needs ~38.3 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q2_K quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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.

DeepSeek R1 Distill 70B at Q4_K_M needs 53.7 GB — too much for MacBook Pro M3 Max 48GB (34.6 GB). Runs at Q2_K (38.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 53.7 GB, exceeds 34.6 GB available

53.7 GB required34.6 GB available

155% VRAM needed

19.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.4 tok/s

TTFT

56329 ms

Safe context

Memory

53.7 GB / 34.6 GB

Offload

40%

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek R1 Distill 70B on MacBook Pro M3 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: 3.4 tok/s decode · 56.3s TTFT (warm) · 9 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 2.6 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	F	Too heavy	3.6 tok/s	29146 ms	4K
Coding	F	Too heavy	3.2 tok/s	61258 ms	4K
Agentic Coding	F	Too heavy	3.1 tok/s	90294 ms	4K
Reasoning	F	Too heavy	3.4 tok/s	66571 ms	4K
RAG	F	Too heavy	3.1 tok/s	112868 ms	4K

Quantization options

How DeepSeek R1 Distill 70B (70B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	F0
Q3_K_S	3	34.3 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 70B on your machine.

Run

ollama run deepseek-r1:70b

Upgrade options

Hardware that runs DeepSeek R1 Distill 70B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)546 GB/s (+146)

Makes the model fit on the accelerator instead of staying completely out of reach.15.3 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.

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.6.1 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.

~$2,499 MSRP

MacBook Pro M2 Max 96GBApple upgrade

96 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.5.9 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.

~$3,199 MSRP

NVIDIA H100 80GBBiggest leap

80 GB VRAM (+32)3350 GB/s (+2950)

Makes the model fit on the accelerator instead of staying completely out of reach.71.7 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.

~$40,000 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 48GB run DeepSeek R1 Distill 70B?

Yes, MacBook Pro M3 Max 48GB can run DeepSeek R1 Distill 70B at Q2_K quantization (Very compromised (needs ~2.6 GB host RAM)). The recommended Q4_K_M requires 53.7 GB which exceeds available memory, but at Q2_K it needs only 38.3 GB. Expected decode speed: 6.9 tok/s.

How much VRAM does DeepSeek R1 Distill 70B need?

DeepSeek R1 Distill 70B (70B parameters) requires approximately 53.7 GB at Q4_K_M quantization. On MacBook Pro M3 Max 48GB, it fits at Q2_K using 38.3 GB.

What is the best quantization for DeepSeek R1 Distill 70B?

The recommended quantization is Q4_K_M, but on MacBook Pro M3 Max 48GB the best fitting quantization is Q2_K, which uses 38.3 GB.

What speed will DeepSeek R1 Distill 70B run at on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, DeepSeek R1 Distill 70B achieves approximately 6.9 tokens per second decode speed with a time-to-first-token of 28177ms using Q2_K quantization.

Can MacBook Pro M3 Max 48GB run DeepSeek R1 Distill 70B for coding?

For coding workloads, DeepSeek R1 Distill 70B on MacBook Pro M3 Max 48GB receives a F grade with 3.2 tok/s and 4K context.

What context window can DeepSeek R1 Distill 70B use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, DeepSeek R1 Distill 70B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 48GB See all hardware for DeepSeek R1 Distill 70B

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