Can StarCoder2 7B run on MacBook Pro M4 32GB?

Q: Is unified memory on MacBook Pro M4 32GB as fast as VRAM for StarCoder2 7B?

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.

YES — Runs Great

C45Usable

Estimated — low-sample bucket· few comparable runs

StarCoder2 7B needs ~9.4 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) — 9.4 GB, 18.6 tok/s, Runs well

9.4 GB required23.0 GB available

41% VRAM used

Fit status

Runs well

Decode

18.6 tok/s

TTFT

10400 ms

Safe context

281K

Memory

9.4 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsStarCoder2 7B 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	C	Runs well	18.6 tok/s	5673 ms	281K
Coding	C	Runs well	18.6 tok/s	10400 ms	281K
Agentic Coding	C	Runs well	18.6 tok/s	15127 ms	281K
Reasoning	C	Runs well	18.6 tok/s	12291 ms	281K
RAG	C	Runs well	18.6 tok/s	18909 ms	281K

Quantization options

How StarCoder2 7B (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	C44
Q3_K_S	3	3.4 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

MacBook Pro M4 Pro 64GBBudget pick

64 GB Unified (+32)273 GB/s (+153)

Raises estimated decode speed by about 144%.45.3 tok/s decode

Raises estimated decode speed by about 144%.

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

~$1,599 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 38%.25.6 tok/s decode

Raises estimated decode speed by about 38%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 254%.65.9 tok/s decode

Raises estimated decode speed by about 254%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M4 32GB run StarCoder2 7B?

Yes, MacBook Pro M4 32GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 18.6 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, StarCoder2 7B 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 StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on MacBook Pro M4 32GB receives a C grade with 18.6 tok/s and 281K context.

What context window can StarCoder2 7B use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, StarCoder2 7B can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 32GB as fast as VRAM for StarCoder2 7B?

See all results for MacBook Pro M4 32GB See all hardware for StarCoder2 7B

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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-7b-gguf-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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