Can Granite Code 34B run on MacBook Pro M2 Max 32GB?

YES — With NVFP4

B64Good

○Estimated from fit model

Granite Code 34B needs ~27.1 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With NVFP4 quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Granite Code 34B at Q4_K_M needs 28.8 GB — too much for MacBook Pro M2 Max 32GB (23.0 GB). Runs at NVFP4 (27.1 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.8 GB, exceeds 23.0 GB available

28.8 GB required23.0 GB available

125% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.8 tok/s

TTFT

22025 ms

Safe context

Memory

28.8 GB / 23.0 GB

Offload

20%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 34B on MacBook Pro M2 Max 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: 8.8 tok/s decode · 22.0s TTFT (warm) · 22 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 2.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised	8.8 tok/s	11989 ms	4K
Coding	F	Too heavy	8.1 tok/s	23861 ms	4K
Agentic Coding	F	Too heavy	7.0 tok/s	40008 ms	4K
Reasoning	F	Too heavy	8.1 tok/s	28199 ms	4K
RAG	F	Too heavy	7.0 tok/s	50011 ms	4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A77
Q3_K_SBest for your GPU	3	16.7 GB	Low	A76

Get started

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

Run

ollama run granite-code:34b

Upgrade options

Hardware that runs Granite Code 34B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.8.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple upgrade

36 GB Unified (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.4.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

~$1,999 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Max 32GB See all hardware for Granite Code 34B

URL: https://willitrunai.com/can-run/granite-code-34b-on-m2-max-32gb

⇱

Can Granite Code 34B run on MacBook Pro M2 Max 32GB?

YES — With NVFP4

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Granite Code 34B well

Frequently asked questions