Can StarCoder2 3B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C43Usable

Estimated from fit model

StarCoder2 3B needs ~30.7 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~42 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

Q4_K_M (Medium quality) — 30.7 GB, 42.0 tok/s, Runs well

30.7 GB required184.3 GB available

17% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

7.0M

Memory

30.7 GB / 184.3 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsStarCoder2 3B 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	7.0M
Coding	C	Runs well	42.0 tok/s	4610 ms	7.0M
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	7.0M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	7.0M
RAG	C	Runs well	42.0 tok/s	8381 ms	7.0M

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	D37
Q3_K_S	3	1.5 GB	Low	D37
NVFP4	4	1.7 GB	Medium	D37
Q4_K_M	4	1.8 GB	Medium	D37
Q5_K_M	5	2.2 GB	High	D37
Q6_K	6	2.5 GB	High	D37
Q8_0	8	3.2 GB	Very High	D37
F16Best for your GPU	16	6.1 GB	Maximum	D37

Get started

Copy-paste commands to run StarCoder2 3B on your machine.

Run

lms load hf-second-state--starcoder2-3b-gguf && lms server start

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run StarCoder2 3B?

Yes, Mac Studio M3 Ultra 256GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 30.7 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on Mac Studio M3 Ultra 256GB receives a C grade with 42.0 tok/s and 7.0M context.

What context window can StarCoder2 3B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, StarCoder2 3B can safely use up to 7.0M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for StarCoder2 3B?

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 3B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-3b-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: