Will It Run AI

Can Granite Code 34B run on MacBook Pro M4 Pro 24GB?

YES — With Q2_K

B60Good
Estimated — low-sample bucket· few comparable runs

Granite Code 34B needs ~20.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q2_K quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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 27.9 GB — too much for MacBook Pro M4 Pro 24GB (17.3 GB). Runs at Q2_K (20.4 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.9 GB, exceeds 17.3 GB available
27.9 GB required17.3 GB available
161% VRAM needed

10.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.6 tok/s

TTFT

18195 ms

Safe context

4K

Memory

27.9 GB / 17.3 GB

Offload

40%

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGranite Code 34B on MacBook Pro M4 Pro 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: 10.6 tok/s decode · 18.2s TTFT (warm) · 27 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 20% 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 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy11.5 tok/s9194 ms4K
CodingFToo heavy10.6 tok/s18195 ms4K
Agentic CodingFToo heavy9.3 tok/s30326 ms4K
ReasoningFToo heavy10.6 tok/s21503 ms4K
RAGFToo heavy9.3 tok/s37908 ms4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowF0
Q3_K_S
3
16.7 GB
LowF0
NVFP4
4
19.0 GB
MediumF0
Q4_K_M
4
20.7 GB
MediumF0
Q5_K_M
5
24.5 GB
HighF0
Q6_K
6
27.9 GB
HighF0
Q8_0
8
36.4 GB
Very HighF0
F16
16
69.7 GB
MaximumF0

Get started

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

Run

ollama run granite-code:34b

Upgrade-Optionen

Hardware, die Granite Code 34B gut ausführt

Mac mini M4 64GBGünstige Wahl
64 GB Unified (+40)
A
Makes the model fit on the accelerator instead of staying completely out of reach.8.1 tok/s Dekodierung

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.

ca. $1,099 MSRP

MacBook Pro M4 Pro 64GBBestes Preis-Leistungs-Verhältnis
64 GB Unified (+40)
A
Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s Dekodierung

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.

ca. $1,599 MSRP

MacBook Pro M3 Pro 36GBApple-Upgrade
36 GB Unified (+12)
B
Makes the model fit on the accelerator instead of staying completely out of reach.4.7 tok/s Dekodierung

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

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run Granite Code 34B?

Yes, MacBook Pro M4 Pro 24GB can run Granite Code 34B at Q2_K quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 27.9 GB which exceeds available memory, but at Q2_K it needs only 20.4 GB. Expected decode speed: 20.4 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 27.9 GB at Q4_K_M quantization. On MacBook Pro M4 Pro 24GB, it fits at Q2_K using 20.4 GB.

What is the best quantization for Granite Code 34B?

The recommended quantization is Q4_K_M, but on MacBook Pro M4 Pro 24GB the best fitting quantization is Q2_K, which uses 20.4 GB.

What speed will Granite Code 34B run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Granite Code 34B achieves approximately 20.4 tokens per second decode speed with a time-to-first-token of 9486ms using Q2_K quantization.

Can MacBook Pro M4 Pro 24GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on MacBook Pro M4 Pro 24GB receives a F grade with 10.6 tok/s and 4K context.

What context window can Granite Code 34B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Granite Code 34B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on MacBook Pro M4 Pro 24GB?

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.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for Granite Code 34B?

Not always. MacBook Pro M4 Pro 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 MacBook Pro M4 Pro 24GBSee all hardware for Granite Code 34B
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