Can DeepSeek Coder V2 16B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A80Great

○Estimated from fit model

DeepSeek Coder V2 16B needs ~20.9 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~113 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 20.9 GB, 113.2 tok/s, Runs well

20.9 GB required46.1 GB available

45% VRAM used

Fit status

Runs well

Decode

113.2 tok/s

TTFT

1710 ms

Safe context

131K

Memory

20.9 GB / 46.1 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on Mac Studio M2 Ultra 64GB
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: 113.2 tok/s decode · 1.7s TTFT (warm) · 283 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	113.2 tok/s	933 ms	131K
Coding	A	Runs well	113.2 tok/s	1710 ms	131K
Agentic Coding	A	Runs well	113.2 tok/s	2488 ms	131K
Reasoning	A	Runs well	113.2 tok/s	2021 ms	131K
RAG	A	Runs well	113.2 tok/s	3110 ms	131K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A71
Q3_K_S	3	7.8 GB	Low	A71
NVFP4	4	9.0 GB	Medium	A71
Q4_K_M	4	9.8 GB	Medium	A72
Q5_K_M	5	11.5 GB	High	A72
Q6_K	6	13.1 GB	High	A73
Q8_0	8	17.1 GB	Very High	A74
F16Best for your GPU	16	32.8 GB	Maximum	A76

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 Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	23.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for DeepSeek Coder V2 16B

URL: https://willitrunai.com/can-run/deepseek-coder-v2-16b-on-m2-ultra-64gb

⇱ DeepSeek Coder V2 16B on Mac Studio M2 Ultra 64GB? YES