Can DeepSeek Coder V2 16B run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

A80Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~19.1 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~51 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) — 19.1 GB, 51.3 tok/s, Runs well

19.1 GB required34.6 GB available

55% VRAM used

Fit status

Runs well

Decode

51.3 tok/s

TTFT

3775 ms

Safe context

91K

Memory

19.1 GB / 34.6 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on MacBook Pro M4 Pro 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: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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	51.3 tok/s	2059 ms	91K
Coding	A	Runs well	51.3 tok/s	3775 ms	91K
Agentic Coding	A	Runs well	51.3 tok/s	5491 ms	91K
Reasoning	A	Runs well	51.3 tok/s	4462 ms	91K
RAG	A	Runs well	51.3 tok/s	6864 ms	91K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A72
Q3_K_S	3	7.8 GB	Low	A73
NVFP4	4	9.0 GB	Medium	A73
Q4_K_M	4	9.8 GB	Medium	A74
Q5_K_M	5	11.5 GB	High	A74
Q6_K	6	13.1 GB	High	A75
Q8_0Best for your GPU	8	17.1 GB	Very High	A77
F16	16	32.8 GB	Maximum	F0

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 M4 Pro 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	31.8 tok/s
Qwen 3.5 27B	27B	S	22.7 tok/s
Qwen 3.6 27B	27B	S	17.3 tok/s
Qwen 3.6 35B A3B	35B	S	29.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	32.9 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run DeepSeek Coder V2 16B?

Yes, MacBook Pro M4 Pro 48GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 51.3 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 19.1 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 M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, DeepSeek Coder V2 16B achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3775ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on MacBook Pro M4 Pro 48GB receives a A grade with 51.3 tok/s and 91K context.

What context window can DeepSeek Coder V2 16B use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, DeepSeek Coder V2 16B can safely use up to 91K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for DeepSeek Coder V2 16B?

Not always. MacBook Pro M4 Pro 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.

See all results for MacBook Pro M4 Pro 48GB See all hardware for DeepSeek Coder V2 16B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/deepseek-coder-v2-16b-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: