Can StarCoder2 7B run on MacBook Pro M3 Max 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

StarCoder2 7B needs ~12.9 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 12.9 GB, 56.2 tok/s, Runs well

12.9 GB required46.1 GB available

28% VRAM used

Fit status

Runs well

Decode

56.2 tok/s

TTFT

3444 ms

Safe context

663K

Memory

12.9 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on MacBook Pro M3 Max 64GB
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: 56.2 tok/s decode · 3.4s TTFT (warm) · 141 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	56.2 tok/s	1879 ms	663K
Coding	C	Runs well	56.2 tok/s	3444 ms	663K
Agentic Coding	C	Runs well	56.2 tok/s	5010 ms	663K
Reasoning	C	Runs well	56.2 tok/s	4071 ms	663K
RAG	C	Runs well	56.2 tok/s	6263 ms	663K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4	3.9 GB	Medium	C41
Q4_K_M	4	4.3 GB	Medium	C42
Q5_K_M	5	5.0 GB	High	C42
Q6_K	6	5.7 GB	High	C42
Q8_0	8	7.5 GB	Very High	C42
F16Best for your GPU	16	14.3 GB	Maximum	C44

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

アップグレードオプション

StarCoder2 7Bを快適に動かすハードウェア

MacBook Pro M4 Max 96GBコスパ重視

96 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 56%.87.8 tok/sデコード

Raises estimated decode speed by about 56%.

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

〜$2,499 MSRP

Mac Studio M3 Ultra 96GBコスパ最良

96 GB Unified (+32)819 GB/s (+419)

Raises estimated decode speed by about 74%.98 tok/sデコード

Raises estimated decode speed by about 74%.

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

〜$3,999 MSRP

Mac Studio M2 Ultra 128GBAppleアップグレード

128 GB Unified (+64)800 GB/s (+400)

Raises estimated decode speed by about 74%.98 tok/sデコード

Raises estimated decode speed by about 74%.

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

〜$3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 64GB run StarCoder2 7B?

Yes, MacBook Pro M3 Max 64GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 56.2 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, StarCoder2 7B achieves approximately 56.2 tokens per second decode speed with a time-to-first-token of 3444ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on MacBook Pro M3 Max 64GB receives a C grade with 56.2 tok/s and 663K context.

What context window can StarCoder2 7B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, StarCoder2 7B can safely use up to 663K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for StarCoder2 7B?

Not always. MacBook Pro M3 Max 64GB 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 M3 Max 64GB See all hardware for StarCoder2 7B

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