Can Granite Code 34B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A79Great

○Estimated from fit model

Granite Code 34B needs ~32.2 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 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) — 32.2 GB, 24.2 tok/s, Runs well

32.2 GB required46.1 GB available

70% VRAM used

Fit status

Runs well

Decode

24.2 tok/s

TTFT

7988 ms

Safe context

Memory

32.2 GB / 46.1 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGranite Code 34B 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: 24.2 tok/s decode · 8.0s TTFT (warm) · 61 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	22.4 tok/s	4720 ms	8K
Coding	A	Runs well	22.4 tok/s	8654 ms	8K
Agentic Coding	A	Runs well	22.4 tok/s	12587 ms	8K
Reasoning	A	Runs well	22.4 tok/s	10227 ms	8K
RAG	A	Runs well	22.4 tok/s	15734 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A71
Q3_K_S	3	16.7 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59 tok/s
👁 Alibaba

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for Granite Code 34B

URL: https://willitrunai.com/can-run/granite-code-34b-on-m2-ultra-64gb

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