Can CodeLlama 13B Instruct run on Mac mini M4 64GB?

YES — Runs Great

A73Great

Estimated — low-sample bucket· few comparable runs

CodeLlama 13B Instruct needs ~27.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 27.9 GB, 9.6 tok/s, Runs well

27.9 GB required46.1 GB available

61% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20192 ms

Safe context

16K

Memory

27.9 GB / 46.1 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on Mac mini M4 64GB
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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	9.6 tok/s	11014 ms	16K
Coding	A	Runs well	9.6 tok/s	20192 ms	16K
Agentic Coding	A	Tight fit	9.6 tok/s	29370 ms	16K
Reasoning	A	Runs well	9.6 tok/s	23863 ms	16K
RAG	A	Tight fit	9.6 tok/s	36713 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B67
Q3_K_S	3	6.4 GB	Low	B67
NVFP4	4	7.3 GB	Medium	B67
Q4_K_M	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	B68
Q6_K	6	10.7 GB	High	B68
Q8_0	8	13.9 GB	Very High	B69
F16Best for your GPU	16	26.7 GB	Maximum	A73

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Your hardware

More models your Mac mini M4 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	13.1 tok/s
Qwen 3.5 27B	27B	S	9.3 tok/s
Qwen 3.6 27B	27B	S	7.1 tok/s
Qwen 3.6 35B A3B	35B	S	12.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	13.5 tok/s

Frequently asked questions

Can Mac mini M4 64GB run CodeLlama 13B Instruct?

Yes, Mac mini M4 64GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 27.9 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 M4 64GB?

On Mac mini M4 64GB, CodeLlama 13B Instruct achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20192ms using Q4_K_M quantization.

Can Mac mini M4 64GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Mac mini M4 64GB receives a A grade with 9.6 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on Mac mini M4 64GB?

On Mac mini M4 64GB, CodeLlama 13B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for CodeLlama 13B Instruct?

Not always. Mac mini M4 64GB 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 64GB See all hardware for CodeLlama 13B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/codellama-13b-instruct-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

9.6 tok/s

11014 ms

16K

Coding

Runs well

9.6 tok/s

20192 ms

16K

Agentic Coding

Tight fit

9.6 tok/s

29370 ms

16K

Reasoning

Runs well

9.6 tok/s

23863 ms

16K

RAG

Tight fit

9.6 tok/s

36713 ms

16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B67
Q3_K_S	3	6.4 GB	Low	B67
NVFP4	4	7.3 GB	Medium	B67
Q4_K_M	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	B68
Q6_K	6	10.7 GB	High	B68
Q8_0	8	13.9 GB	Very High	B69
F16Best for your GPU	16	26.7 GB	Maximum	A73

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

13.1 tok/s

Qwen 3.5 27B

27B

9.3 tok/s

Qwen 3.6 27B

27B

7.1 tok/s

Qwen 3.6 35B A3B

35B

12.1 tok/s

Qwen3-VL 30B A3B Instruct

30B

13.5 tok/s

Frequently asked questions

Can Mac mini M4 64GB run CodeLlama 13B Instruct?

Yes, Mac mini M4 64GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 27.9 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 M4 64GB?

On Mac mini M4 64GB, CodeLlama 13B Instruct achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20192ms using Q4_K_M quantization.

Can Mac mini M4 64GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Mac mini M4 64GB receives a A grade with 9.6 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on Mac mini M4 64GB?

On Mac mini M4 64GB, CodeLlama 13B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for CodeLlama 13B Instruct?