Is unified memory on MacBook Pro M4 32GB as fast as VRAM for CodeLlama 7B Instruct?

Not always. MacBook Pro M4 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 7B Instruct run on MacBook Pro M4 32GB?

YES — Runs Great

A75Great

Estimated — low-sample bucket· few comparable runs

CodeLlama 7B Instruct needs ~16.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~19 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) — 16.4 GB, 18.6 tok/s, Runs well

16.4 GB required23.0 GB available

71% VRAM used

Fit status

Runs well

Decode

18.6 tok/s

TTFT

10400 ms

Safe context

16K

Memory

16.4 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on MacBook Pro M4 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 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	A	Runs well	18.6 tok/s	5673 ms	16K
Coding	A	Runs well	18.6 tok/s	10400 ms	16K
Agentic Coding	A	Runs with offload (needs ~0.2 GB host RAM)	16.9 tok/s	16640 ms	16K
Reasoning	A	Runs well	18.6 tok/s	12291 ms	16K
RAG	A	Runs with offload (needs ~0.2 GB host RAM)	16.9 tok/s	20801 ms

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B68
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Your hardware

More models your MacBook Pro M4 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
Qwen 3.5 27B	27B	S	8.6 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run CodeLlama 7B Instruct?

Yes, MacBook Pro M4 32GB can run CodeLlama 7B Instruct with a A grade (Runs well). Expected decode speed: 18.6 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 7B Instruct run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, CodeLlama 7B Instruct achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10400ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on MacBook Pro M4 32GB receives a A grade with 18.6 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, CodeLlama 7B 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.

See all results for MacBook Pro M4 32GB See all hardware for CodeLlama 7B Instruct

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