Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for CodeLlama 13B Instruct?

Not always. MacBook Pro M2 Max 32GB 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 CodeLlama 13B Instruct run on MacBook Pro M2 Max 32GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on MacBook Pro M2 Max 32GB?

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 Offload

B65Good

Estimated from fit model

CodeLlama 13B Instruct needs ~24.5 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.5 GB, 26.2 tok/s, Runs with offload (needs ~0.5 GB host RAM)

24.5 GB required23.0 GB available

107% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.5 GB host RAM)

Decode

26.2 tok/s

TTFT

7389 ms

Safe context

14K

Memory

24.5 GB / 23.0 GB

Offload

10%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on MacBook Pro M2 Max 32GB
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: 26.2 tok/s decode · 7.4s TTFT (warm) · 66 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 10% 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 0.5 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	A	Runs well	29.3 tok/s	3610 ms	14K
Coding	B	Runs with offload (needs ~0.5 GB host RAM)	26.2 tok/s	7389 ms	14K
Agentic Coding	F	Too heavy	16.0 tok/s	17615 ms	14K
Reasoning	B	Runs with offload (needs ~0.5 GB host RAM)	26.2 tok/s	8732 ms	14K
RAG	F	Too heavy	16.0 tok/s	22018 ms

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A71
Q3_K_S	3	6.4 GB	Low	A72
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple upgrade

36 GB Unified (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.13.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

RTX 5090 32GBBiggest leap

1792 GB/s (+1392)

Removes host-memory offload, which is usually the single biggest latency and throughput win.146.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 461%.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 32GB run CodeLlama 13B Instruct?

Yes, MacBook Pro M2 Max 32GB can run CodeLlama 13B Instruct with a B grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 26.2 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 24.5 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 M2 Max 32GB?

On MacBook Pro M2 Max 32GB, CodeLlama 13B Instruct achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7389ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on MacBook Pro M2 Max 32GB receives a B grade with 26.2 tok/s and 14K context.

What context window can CodeLlama 13B Instruct use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, CodeLlama 13B Instruct can safely use up to 14K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Max 32GB See all hardware for CodeLlama 13B Instruct

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