Is unified memory on Mac mini M2 24GB as fast as VRAM for Granite Code 34B?

Not always. Mac mini M2 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.

Can Granite Code 34B run on Mac mini M2 24GB?

Q: What should I upgrade first if Granite Code 34B feels slow on Mac mini M2 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.

YES — With Q2_K

C55Usable

Estimated from fit model

Granite Code 34B needs ~20.4 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q2_K quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very 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 27.9 GB — too much for Mac mini M2 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

2.0 tok/s

TTFT

96800 ms

Safe context

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 Mac mini M2 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Granite Code 34B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 34B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~63 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	62.7	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	31.4	Fits

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	F0
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4

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 (+40)120 GB/s (+20)

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 (+40)273 GB/s (+173)

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

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Makes the model fit on the accelerator instead of staying completely out of reach.24.2 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.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Granite Code 34B?

Yes, Mac mini M2 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: 3.5 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 Mac mini M2 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 Mac mini M2 24GB the best fitting quantization is Q2_K, which uses 20.4 GB.

What speed will Granite Code 34B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Granite Code 34B achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 55223ms using Q2_K quantization.

Can Mac mini M2 24GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on Mac mini M2 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Granite Code 34B use on Mac mini M2 24GB?

On Mac mini M2 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.

See all results for Mac mini M2 24GB See all hardware for Granite Code 34B

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