Can CodeLlama 13B Instruct run on Mac mini M2 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeLlama 13B Instruct needs ~23.6 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 23.6 GB, exceeds 17.3 GB available

23.6 GB required17.3 GB available

136% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36308 ms

Safe context

Memory

23.6 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct 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: 5.3 tok/s decode · 36.3s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 23.6 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.1 GB host RAM)	7.9 tok/s	13321 ms	8K
Coding	F	Too heavy	5.3 tok/s	36308 ms	8K
Agentic Coding	F	Too heavy	3.7 tok/s	76345 ms	8K
Reasoning	F	Too heavy	5.3 tok/s	42909 ms	8K
RAG	F	Too heavy	3.7 tok/s	95431 ms	8K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A73
Q3_K_S	3	6.4 GB	Low	A74
NVFP4	4	7.3 GB	Medium	A75
Q4_K_M	4	7.9 GB	Medium	A76
Q5_K_M	5	9.4 GB	High	A76
Q6_KBest for your GPU	6	10.7 GB	High	A76
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien CodeLlama 13B Instruct

MacBook Pro M4 32GBOpción económica

32 GB Unified (+8)120 GB/s (+20)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.6 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 62%.

~$799 MSRP

Mac mini M4 64GBMejor relación calidad-precio

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

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.9.6 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

Mac mini M4 32GBMejora Apple

32 GB Unified (+8)120 GB/s (+20)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.6 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 62%.

~$1,099 MSRP

RTX 5090 32GBMayor salto

32 GB VRAM (+8)1792 GB/s (+1692)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.146.9 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.

~$1,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than Mac mini M2 24GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 23.6 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on Mac mini M2 24GB?

On Mac mini M2 24GB, CodeLlama 13B Instruct achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36308ms using Q4_K_M quantization.

Can Mac mini M2 24GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Mac mini M2 24GB receives a F grade with 5.3 tok/s and 8K context.

What context window can CodeLlama 13B Instruct use on Mac mini M2 24GB?

On Mac mini M2 24GB, CodeLlama 13B Instruct can safely use up to 8K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on Mac mini M2 24GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on Mac mini M2 24GB as fast as VRAM for CodeLlama 13B Instruct?

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.

See all results for Mac mini M2 24GB See all hardware for CodeLlama 13B Instruct

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