Can SQLCoder 7B run on Mac mini M2 24GB?

YES — Runs Great

A78Great

Estimated from fit model

SQLCoder 7B needs ~9.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.7 GB, 16.4 tok/s, Runs well

9.7 GB required17.3 GB available

56% VRAM used

Fit status

Runs well

Decode

16.4 tok/s

TTFT

11831 ms

Safe context

Memory

9.7 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsSQLCoder 7B 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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	A	Runs well	15.2 tok/s	6937 ms	8K
Coding	A	Runs well	15.2 tok/s	12718 ms	8K
Agentic Coding	A	Runs well	15.2 tok/s	18499 ms	8K
Reasoning	A	Runs well	15.2 tok/s	15030 ms	8K
RAG	A	Runs well	15.2 tok/s	23124 ms	8K

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A76
Q3_K_S	3	3.4 GB	Low	A76
NVFP4	4

Get started

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

Run

ollama run sqlcoder

Your hardware

More models your Mac mini M2 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	12.7 tok/s
Magistral Small 2507	24B	B	3.7 tok/s

Frequently asked questions

Can Mac mini M2 24GB run SQLCoder 7B?

Yes, Mac mini M2 24GB can run SQLCoder 7B with a A grade (Runs well). Expected decode speed: 15.2 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for SQLCoder 7B?

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

What speed will SQLCoder 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, SQLCoder 7B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.

Can Mac mini M2 24GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on Mac mini M2 24GB receives a A grade with 15.2 tok/s and 8K context.

What context window can SQLCoder 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, SQLCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for SQLCoder 7B?

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.

See all results for Mac mini M2 24GB See all hardware for SQLCoder 7B

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