Will It Run AI

Can Granite Code 34B run on Mac mini M4 32GB?

YES — With NVFP4

B63Good
Estimated — low-sample bucket· few comparable runs

Granite Code 34B needs ~27.1 GB VRAM. Mac mini M4 32GB has 23.0 GB. With NVFP4 quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very 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 28.8 GB — too much for Mac mini M4 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

5.9 tok/s

TTFT

32857 ms

Safe context

4K

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 Mac mini M4 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: 5.9 tok/s decode · 32.9s TTFT (warm) · 15 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

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~3 GB host RAM)6.4 tok/s16509 ms4K
CodingFToo heavy5.9 tok/s32857 ms4K
Agentic CodingFToo heavy5.1 tok/s55092 ms4K
ReasoningFToo heavy5.9 tok/s38831 ms4K
RAGFToo heavy5.1 tok/s68865 ms4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowA77
Q3_K_SBest for your GPU
3
16.7 GB
LowA76
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

Opciones de mejora

Hardware que ejecuta bien Granite Code 34B

Mac mini M4 64GBOpción económica
64 GB Unified (+32)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBMejor relación calidad-precio
64 GB Unified (+32)273 GB/s (+153)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.19.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Mac Studio M2 Ultra 64GBMejora Apple
64 GB Unified (+32)800 GB/s (+680)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.24.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run Granite Code 34B?

Yes, Mac mini M4 32GB can run Granite Code 34B at NVFP4 quantization (Very compromised (needs ~2.8 GB host RAM)). The recommended Q4_K_M requires 28.8 GB which exceeds available memory, but at NVFP4 it needs only 27.1 GB. Expected decode speed: 7.3 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 28.8 GB at Q4_K_M quantization. On Mac mini M4 32GB, it fits at NVFP4 using 27.1 GB.

What is the best quantization for Granite Code 34B?

The recommended quantization is Q4_K_M, but on Mac mini M4 32GB the best fitting quantization is NVFP4, which uses 27.1 GB.

What speed will Granite Code 34B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, Granite Code 34B achieves approximately 7.3 tokens per second decode speed with a time-to-first-token of 26627ms using NVFP4 quantization.

Can Mac mini M4 32GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on Mac mini M4 32GB receives a F grade with 5.9 tok/s and 4K context.

What context window can Granite Code 34B use on Mac mini M4 32GB?

On Mac mini M4 32GB, Granite Code 34B can safely use up to 4K tokens of context at NVFP4 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 Mac mini M4 32GB?

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 Mac mini M4 32GB as fast as VRAM for Granite Code 34B?

Not always. Mac mini M4 32GB 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 M4 32GBSee all hardware for Granite Code 34B
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