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URL: https://willitrunai.com/can-run/codellama-13b-instruct-on-m4-pro-24gb

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Can CodeLlama 13B Instruct run on MacBook Pro M4 Pro 24GB?

NO — Won't Fit

F0Won't run
▲Estimated — low-sample bucket· few comparable runs

CodeLlama 13B Instruct needs ~23.6 GB but MacBook Pro M4 Pro 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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.

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

15.2 tok/s

TTFT

12758 ms

Safe context

8K

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 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: 15.2 tok/s decode · 12.8s TTFT (warm) · 38 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~0.1 GB host RAM)22.6 tok/s4681 ms8K
CodingFToo heavy17.2 tok/s11227 ms8K
Agentic CodingFToo heavy10.5 tok/s26826 ms8K
ReasoningFToo heavy15.2 tok/s15077 ms8K
RAGFToo heavy10.5 tok/s33533 ms8K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowA73
Q3_K_S
3
6.4 GB
LowA74
NVFP4
4

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.6 tok/s decode

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.

~$799 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)
A
Makes the model fit on the accelerator instead of staying completely out of reach.9.6 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

Mac mini M4 32GBApple upgrade
32 GB Unified (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.6 tok/s decode

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.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+8)1792 GB/s (+1519)
A
Makes the model fit on the accelerator instead of staying completely out of reach.146.9 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.

~$1,999 MSRP

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GBSee all hardware for CodeLlama 13B Instruct
7.3 GB
Medium
A75
Q4_K_M
4
7.9 GB
MediumA76
Q5_K_M
5
9.4 GB
HighA76
Q6_KBest for your GPU
6
10.7 GB
HighA76
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

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.