Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for DeepSeek Coder V2 16B?

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 DeepSeek Coder V2 16B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

A82Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~17.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~34 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) — 17.4 GB, 34.2 tok/s, Runs well

17.4 GB required23.0 GB available

76% VRAM used

Fit status

Runs well

Decode

34.2 tok/s

TTFT

5669 ms

Safe context

43K

Memory

17.4 GB / 23.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B 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: 34.2 tok/s decode · 5.7s TTFT (warm) · 85 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	A	Runs well	34.2 tok/s	3092 ms	43K
Coding	A	Runs well	34.2 tok/s	5669 ms	43K
Agentic Coding	A	Tight fit	34.2 tok/s	8245 ms	43K
Reasoning	A	Runs well	34.2 tok/s	6699 ms	43K
RAG	A	Tight fit	34.2 tok/s	10307 ms	43K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A75
Q3_K_S	3	7.8 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your MacBook Pro M2 Pro 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	19 tok/s
Qwen 3.5 27B	27B	S	8.5 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run DeepSeek Coder V2 16B?

Yes, MacBook Pro M2 Pro 32GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 34.2 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 17.4 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, DeepSeek Coder V2 16B achieves approximately 34.2 tokens per second decode speed with a time-to-first-token of 5669ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on MacBook Pro M2 Pro 32GB receives a A grade with 34.2 tok/s and 43K context.

What context window can DeepSeek Coder V2 16B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, DeepSeek Coder V2 16B can safely use up to 43K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 32GB See all hardware for DeepSeek Coder V2 16B

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