Can DeepSeek Coder V2 16B run on Mac mini M2 24GB?

YES — With Offload

A77Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~16.5 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 16.5 GB, 15.9 tok/s, Runs with offload

16.5 GB required17.3 GB available

95% VRAM used

Fit status

Runs with offload

Decode

15.9 tok/s

TTFT

12209 ms

Safe context

20K

Memory

16.5 GB / 17.3 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on Mac mini M2 24GB
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: 15.9 tok/s decode · 12.2s TTFT (warm) · 40 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	15.9 tok/s	6660 ms	20K
Coding	A	Runs with offload	15.9 tok/s	12209 ms	20K
Agentic Coding	B	Very compromised (needs ~1.3 GB host RAM)	12.8 tok/s	22048 ms	20K
Reasoning	A	Runs with offload	15.9 tok/s	14429 ms	20K
RAG	B	Very compromised (needs ~1.3 GB host RAM)	12.8 tok/s	27560 ms	20K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A78
Q3_K_S	3	7.8 GB	Low	A79
NVFP4	4	9.0 GB	Medium	A80
Q4_K_M	4	9.8 GB	Medium	A80
Q5_K_M	5	11.5 GB	High	A79
Q6_KBest for your GPU	6	13.1 GB	High	A79
Q8_0	8	17.1 GB	Very High	F0
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 Mac mini M2 24GB can run

Model	Params	Grade	Decode
Magistral Small 2507	24B	B	3.7 tok/s
Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
Devstral Small 1.1	24B	B	3.7 tok/s
GPT-OSS 20B	21B	A	10.9 tok/s
Codestral 2 25.08	22B	B	4.1 tok/s

Frequently asked questions

Can Mac mini M2 24GB run DeepSeek Coder V2 16B?

Yes, Mac mini M2 24GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 15.9 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 16.5 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 Mac mini M2 24GB?

On Mac mini M2 24GB, DeepSeek Coder V2 16B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12209ms using Q4_K_M quantization.

Can Mac mini M2 24GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Mac mini M2 24GB receives a A grade with 15.9 tok/s and 20K context.

What context window can DeepSeek Coder V2 16B use on Mac mini M2 24GB?

On Mac mini M2 24GB, DeepSeek Coder V2 16B can safely use up to 20K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 16B feels slow on Mac mini M2 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on Mac mini M2 24GB as fast as VRAM for DeepSeek Coder V2 16B?

Not always. Mac mini M2 24GB 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 Mac mini M2 24GB See all hardware for DeepSeek Coder V2 16B

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