Can StarCoder2 15B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C46Usable

Estimated from fit model

StarCoder2 15B needs ~40.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q5_K_M quantization, expect ~57 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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

Q5_K_M (High quality) — 40.6 GB, 57.4 tok/s, Runs well

40.6 GB required184.3 GB available

22% VRAM used

Fit status

Runs well

Decode

57.4 tok/s

TTFT

3372 ms

Safe context

16K

Memory

40.6 GB / 184.3 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Mac Studio M3 Ultra 256GB
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: 57.4 tok/s decode · 3.4s TTFT (warm) · 144 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	57.4 tok/s	1839 ms	16K
Coding	C	Runs well	57.4 tok/s	3372 ms	16K
Agentic Coding	C	Runs well	57.4 tok/s	4904 ms	16K
Reasoning	C	Runs well	57.4 tok/s	3985 ms	16K
RAG	C	Runs well	52.6 tok/s	6692 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D38
Q3_K_S	3	7.4 GB	Low	D38
NVFP4	4	8.4 GB	Medium	D38
Q4_K_M	4	9.2 GB	Medium	D38
Q5_K_M	5	10.8 GB	High	D39
Q6_K	6	12.3 GB	High	D39
Q8_0	8	16.1 GB	Very High	D39
F16Best for your GPU	16	30.7 GB	Maximum	C40

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

升级选项

能流畅运行 StarCoder2 15B 的硬件

AMD Instinct MI300X 192GB经济之选

5300 GB/s (+4481)

Raises estimated decode speed by about 266%.210 tok/s 解码

Raises estimated decode speed by about 266%.

~$15,000 MSRP

AMD Instinct MI325X 256GB最佳性价比

6000 GB/s (+5181)

Raises estimated decode speed by about 266%.210 tok/s 解码

Raises estimated decode speed by about 266%.

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

~$20,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run StarCoder2 15B?

Yes, Mac Studio M3 Ultra 256GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 57.4 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 40.6 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, StarCoder2 15B achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3372ms using Q5_K_M quantization.

Can Mac Studio M3 Ultra 256GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Mac Studio M3 Ultra 256GB receives a C grade with 57.4 tok/s and 16K context.

What context window can StarCoder2 15B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for StarCoder2 15B?

Not always. Mac Studio M3 Ultra 256GB 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 Mac Studio M3 Ultra 256GB See all hardware for StarCoder2 15B

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