Can CodeLlama 13B Instruct run on MacBook Pro M3 Pro 36GB?

YES — With Offload

A73Great

Estimated from fit model

CodeLlama 13B Instruct needs ~24.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 24.9 GB, 13.8 tok/s, Runs with offload

24.9 GB required25.9 GB available

96% VRAM used

Fit status

Runs with offload

Decode

13.8 tok/s

TTFT

14021 ms

Safe context

16K

Memory

24.9 GB / 25.9 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on MacBook Pro M3 Pro 36GB
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: 13.8 tok/s decode · 14.0s TTFT (warm) · 35 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	13.8 tok/s	7648 ms	16K
Coding	A	Runs with offload	13.8 tok/s	14021 ms	16K
Agentic Coding	F	Too heavy	8.5 tok/s	33088 ms	16K
Reasoning	A	Runs with offload	13.8 tok/s	16570 ms	16K
RAG	F	Too heavy	8.5 tok/s	41360 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A70
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4	7.3 GB	Medium	A71
Q4_K_M	4	7.9 GB	Medium	A72
Q5_K_M	5	9.4 GB	High	A72
Q6_K	6	10.7 GB	High	A73
Q8_0Best for your GPU	8	13.9 GB	Very High	A75
F16	16	26.7 GB	Maximum	F0

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 MacBook Pro M3 Pro 36GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	16.6 tok/s
Qwen 3.5 27B	27B	S	7.2 tok/s
Qwen 3.6 27B	27B	S	5.5 tok/s
Qwen 3.6 35B A3B	35B	A	12.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	17.1 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run CodeLlama 13B Instruct?

Yes, MacBook Pro M3 Pro 36GB can run CodeLlama 13B Instruct with a A grade (Runs with offload). Expected decode speed: 13.8 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 24.9 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 MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, CodeLlama 13B Instruct achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 14021ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on MacBook Pro M3 Pro 36GB receives a A grade with 13.8 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, 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.

What should I upgrade first if CodeLlama 13B Instruct feels slow on MacBook Pro M3 Pro 36GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for CodeLlama 13B Instruct?

Not always. MacBook Pro M3 Pro 36GB 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 MacBook Pro M3 Pro 36GB See all hardware for CodeLlama 13B Instruct

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Preview:

What limits this setup

This setup is broadly balanced for this model.

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.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

13.8 tok/s

7648 ms

16K

Coding

Runs with offload

13.8 tok/s

14021 ms

16K

Agentic Coding

Too heavy

8.5 tok/s

33088 ms

16K

Reasoning

Runs with offload

13.8 tok/s

16570 ms

16K

RAG

Too heavy

8.5 tok/s

41360 ms

16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A70
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4	7.3 GB	Medium	A71
Q4_K_M	4	7.9 GB	Medium	A72
Q5_K_M	5	9.4 GB	High	A72
Q6_K	6	10.7 GB	High	A73
Q8_0Best for your GPU	8	13.9 GB	Very High	A75
F16	16	26.7 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

16.6 tok/s

Qwen 3.5 27B

27B

7.2 tok/s

Qwen 3.6 27B

27B

5.5 tok/s

Qwen 3.6 35B A3B

35B

12.1 tok/s

Qwen3-VL 30B A3B Instruct

30B

17.1 tok/s