Can StarCoder2 7B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

C50Usable

Estimated — low-sample bucket· few comparable runs

StarCoder2 7B needs ~8.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 8.6 GB, 45.3 tok/s, Runs well

8.6 GB required17.3 GB available

50% VRAM used

Fit status

Runs well

Decode

45.3 tok/s

TTFT

4275 ms

Safe context

186K

Memory

8.6 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on MacBook Pro M4 Pro 24GB
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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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	45.3 tok/s	2332 ms	186K
Coding	C	Runs well	45.3 tok/s	4275 ms	186K
Agentic Coding	C	Runs well	45.3 tok/s	6218 ms	186K
Reasoning	C	Runs well	45.3 tok/s	5052 ms	186K
RAG	C	Runs well	45.3 tok/s	7772 ms	186K

Inference speed

StarCoder2 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C47
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C48
Q8_0Best for your GPU	8	7.5 GB	Very High	C50
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

アップグレードオプション

StarCoder2 7Bを快適に動かすハードウェア

RX 7900 XT 20GBコスパ重視

800 GB/s (+527)

Raises estimated decode speed by about 116%.98 tok/sデコード

Raises estimated decode speed by about 116%.

〜$899 MSRP

RTX A4500 20GBコスパ最良

640 GB/s (+367)

Raises estimated decode speed by about 116%.98 tok/sデコード

Raises estimated decode speed by about 116%.

〜$2,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run StarCoder2 7B?

Yes, MacBook Pro M4 Pro 24GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 45.3 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 8.6 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 Pro 24GB?

On MacBook Pro M4 Pro 24GB, StarCoder2 7B achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on MacBook Pro M4 Pro 24GB receives a C grade with 45.3 tok/s and 186K context.

What context window can StarCoder2 7B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, StarCoder2 7B can safely use up to 186K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for StarCoder2 7B?

Not always. MacBook Pro M4 Pro 24GB 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 M4 Pro 24GB See all hardware for StarCoder2 7B

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