Can StarCoder2 15B run on MacBook Pro M1 Pro 16GB?

YES — With Q4_K_M

D39Poor

Estimated from fit model

StarCoder2 15B needs ~13.0 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

StarCoder2 15B at Q5_K_M needs 14.6 GB — too much for MacBook Pro M1 Pro 16GB (11.5 GB). Runs at Q4_K_M (13.0 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 14.6 GB, exceeds 11.5 GB available

14.6 GB required11.5 GB available

127% VRAM needed

3.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.5 tok/s

TTFT

20368 ms

Safe context

Memory

14.6 GB / 11.5 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on MacBook Pro M1 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: 9.5 tok/s decode · 20.4s TTFT (warm) · 24 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.0 tok/s	10546 ms	4K
Coding	F	Too heavy	9.5 tok/s	20368 ms	4K
Agentic Coding	F	Too heavy	8.6 tok/s	32576 ms	4K
Reasoning	F	Too heavy	9.5 tok/s	24071 ms	4K
RAG	F	Too heavy	8.6 tok/s	40720 ms	4K

Inference speed

StarCoder2 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~124 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	123.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	79.0	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	71.3	Fits
RTX 3090 24GB	24 GB	Q5_K_M	67.6	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	64.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	57.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	47.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	45.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	32.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	32.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	24.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	22.7	Fits
RTX 4070 12GB	12 GB	Q5_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.0	Fits
RTX 3060 12GB	12 GB	Q5_K_M	12.5	Heavy offload
RTX 4060 8GB	8 GB	Q5_K_M	4.7	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 StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C54
Q3_K_S	3	7.4 GB	Low	C53
NVFP4Best for your GPU	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade-Optionen

Hardware, die StarCoder2 15B gut ausführt

MacBook Pro M4 32GBGünstige Wahl

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.8.2 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $799 MSRP

Mac mini M4 32GBBestes Preis-Leistungs-Verhältnis

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.8.2 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,099 MSRP

MacBook Air M4 24GBApple-Upgrade

24 GB Unified (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.8.2 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,099 MSRP

RTX 4090 24GBGrößter Sprung

24 GB VRAM (+8)1008 GB/s (+808)

Makes the model fit on the accelerator instead of staying completely out of reach.80.8 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,599 MSRP

Frequently asked questions

Can MacBook Pro M1 Pro 16GB run StarCoder2 15B?

Yes, MacBook Pro M1 Pro 16GB can run StarCoder2 15B at Q4_K_M quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 14.6 GB which exceeds available memory, but at Q4_K_M it needs only 13.0 GB. Expected decode speed: 12.8 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.6 GB at Q5_K_M quantization. On MacBook Pro M1 Pro 16GB, it fits at Q4_K_M using 13.0 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q5_K_M, but on MacBook Pro M1 Pro 16GB the best fitting quantization is Q4_K_M, which uses 13.0 GB.

What speed will StarCoder2 15B run at on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, StarCoder2 15B achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15143ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Pro M1 Pro 16GB receives a F grade with 9.5 tok/s and 4K context.

What context window can StarCoder2 15B use on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, StarCoder2 15B can safely use up to 4K tokens of context at Q4_K_M quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on MacBook Pro M1 Pro 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M1 Pro 16GB as fast as VRAM for StarCoder2 15B?

Not always. MacBook Pro M1 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 M1 Pro 16GB See all hardware for StarCoder2 15B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-m1-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: