Can StarCoder2 7B run on MacBook Pro M2 Pro 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

StarCoder2 7B needs ~7.7 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~33 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) — 7.7 GB, 32.8 tok/s, Runs well

7.7 GB required11.5 GB available

67% VRAM used

Fit status

Runs well

Decode

32.8 tok/s

TTFT

5905 ms

Safe context

90K

Memory

7.7 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on MacBook Pro M2 Pro 16GB
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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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	32.8 tok/s	3221 ms	90K
Coding	C	Runs well	32.8 tok/s	5905 ms	90K
Agentic Coding	C	Runs well	32.8 tok/s	8589 ms	90K
Reasoning	C	Runs well	32.8 tok/s	6978 ms	90K
RAG	C	Runs well	32.8 tok/s	10736 ms	90K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C51
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

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

RX 7700 XT 12GBBudget pick

432 GB/s (+232)

Raises estimated decode speed by about 85%.60.7 tok/s decode

Raises estimated decode speed by about 85%.

~$449 MSRP

RTX 5070 12GBBest value

672 GB/s (+472)

Raises estimated decode speed by about 202%.99.1 tok/s decode

Raises estimated decode speed by about 202%.

~$549 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run StarCoder2 7B?

Yes, MacBook Pro M2 Pro 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 32.8 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 7.7 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 M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, StarCoder2 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on MacBook Pro M2 Pro 16GB receives a C grade with 32.8 tok/s and 90K context.

What context window can StarCoder2 7B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, StarCoder2 7B can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for StarCoder2 7B?

Not always. MacBook Pro M2 Pro 16GB 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 M2 Pro 16GB See all hardware for StarCoder2 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-7b-gguf-on-m2-pro-16gb" 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

32.8 tok/s

3221 ms

90K

Coding

Runs well

32.8 tok/s

5905 ms

90K

Agentic Coding

Runs well

32.8 tok/s

8589 ms

90K

Reasoning

Runs well

32.8 tok/s

6978 ms

90K

RAG

Runs well

32.8 tok/s

10736 ms

90K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C51
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run StarCoder2 7B?

Yes, MacBook Pro M2 Pro 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 32.8 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 7.7 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 M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, StarCoder2 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on MacBook Pro M2 Pro 16GB receives a C grade with 32.8 tok/s and 90K context.

What context window can StarCoder2 7B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, StarCoder2 7B can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for StarCoder2 7B?