Can StarCoder 15B run on MacBook Pro M4 Pro 48GB?

YES — Tight Fit

A74Great

Estimated from fit model

StarCoder 15B needs ~31.8 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q5_K_M quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: 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

Q5_K_M (High quality) — 31.8 GB, 18.3 tok/s, Tight fit

31.8 GB required34.6 GB available

92% VRAM used

Fit status

Tight fit

Decode

18.3 tok/s

TTFT

10554 ms

Safe context

Memory

31.8 GB / 34.6 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsStarCoder 15B on MacBook Pro M4 Pro 48GB
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: 18.3 tok/s decode · 10.6s TTFT (warm) · 46 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	18.3 tok/s	5757 ms	8K
Coding	A	Tight fit	19.9 tok/s	9752 ms	8K
Agentic Coding	F	Too heavy	12.2 tok/s	23143 ms	8K
Reasoning	A	Tight fit	18.3 tok/s	12473 ms	8K
RAG	F	Too heavy	12.2 tok/s	28928 ms	8K

Inference speed

StarCoder 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~113 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	Q5_K_M	113.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	52.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	43.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	41.6	Fits
RTX 4090 24GB	24 GB	Q5_K_M	36.3	Too big
RX 7900 XTX 24GB	24 GB	Q5_K_M	32.8	Too big
RTX 3090 24GB	24 GB	Q5_K_M	31.1	Too big
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	30.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	30.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	22.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	20.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	18.3	Tight
RTX 4080 Super 16GB	16 GB	Q5_K_M	13.1	Too big
RTX 4070 12GB	12 GB	Q5_K_M	5.4	Too big
RTX 3060 12GB	12 GB	Q5_K_M	3.4	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.8	Too big

Estimates for single-stream decoding at Q5_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 StarCoder 15B (15B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B69
Q3_K_S	3	7.4 GB	Low	B69
NVFP4	4	8.4 GB	Medium	B70
Q4_K_M	4	9.2 GB	Medium	B70
Q5_K_M	5	10.8 GB	High	A71
Q6_K	6	12.3 GB	High	A71
Q8_0Best for your GPU	8	16.1 GB	Very High	A73
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Your hardware

More models your MacBook Pro M4 Pro 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	31.8 tok/s
Qwen 3.5 27B	27B	S	22.7 tok/s
Qwen 3.6 27B	27B	S	22.8 tok/s
Qwen 3.6 35B A3B	35B	S	29.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	32.9 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run StarCoder 15B?

Yes, MacBook Pro M4 Pro 48GB can run StarCoder 15B with a A grade (Tight fit). Expected decode speed: 19.9 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 31.8 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, StarCoder 15B achieves approximately 19.9 tokens per second decode speed with a time-to-first-token of 9752ms using Q5_K_M quantization.

Can MacBook Pro M4 Pro 48GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on MacBook Pro M4 Pro 48GB receives a A grade with 19.9 tok/s and 8K context.

What context window can StarCoder 15B use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, StarCoder 15B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for StarCoder 15B?

Not always. MacBook Pro M4 Pro 48GB 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 48GB See all hardware for StarCoder 15B

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