Is unified memory on Mac mini M2 24GB as fast as VRAM for starcoder2 15b instruct v0.1?

Not always. Mac mini M2 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.

Can starcoder2 15b instruct v0.1 run on Mac mini M2 24GB?

Q: What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on Mac mini M2 24GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Tight Fit

C45Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~14.4 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.4 GB, 7.1 tok/s, Tight fit

14.4 GB required17.3 GB available

83% VRAM used

Fit status

Tight fit

Decode

7.1 tok/s

TTFT

27253 ms

Safe context

42K

Memory

14.4 GB / 17.3 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on Mac mini M2 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: 7.1 tok/s decode · 27.3s TTFT (warm) · 18 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	7.1 tok/s	14865 ms	42K
Coding	C	Tight fit	7.1 tok/s	27253 ms	42K
Agentic Coding	C	Tight fit	7.1 tok/s	39641 ms	42K
Reasoning	C	Tight fit	7.1 tok/s	32208 ms	42K
RAG	C	Tight fit	7.1 tok/s	49551 ms	42K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.8.7 tok/s decode

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

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.8.7 tok/s decode

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

~$1,099 MSRP

RTX A4500 20GBBiggest leap

640 GB/s (+540)

Raises estimated decode speed by about 669%.54.6 tok/s decode

Raises estimated decode speed by about 669%.

Moves the workload away from shared memory into dedicated accelerator memory.

~$2,000 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Raises estimated decode speed by about 614%.50.7 tok/s decode

Raises estimated decode speed by about 614%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run starcoder2 15b instruct v0.1?

Yes, Mac mini M2 24GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 7.1 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on Mac mini M2 24GB?

On Mac mini M2 24GB, starcoder2 15b instruct v0.1 achieves approximately 7.1 tokens per second decode speed with a time-to-first-token of 27253ms using Q4_K_M quantization.

Can Mac mini M2 24GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on Mac mini M2 24GB receives a C grade with 7.1 tok/s and 42K context.

What context window can starcoder2 15b instruct v0.1 use on Mac mini M2 24GB?

On Mac mini M2 24GB, starcoder2 15b instruct v0.1 can safely use up to 42K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac mini M2 24GB See all hardware for starcoder2 15b instruct v0.1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-bartowski--starcoder2-15b-instruct-v0-1-gguf-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: