Is unified memory on Mac mini M4 64GB as fast as VRAM for CodeNinja 1.0 OpenChat 7B i1?

Not always. Mac mini M4 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.

Can CodeNinja 1.0 OpenChat 7B i1 run on Mac mini M4 64GB?

YES — Runs Great

C42Usable

Estimated — low-sample bucket· few comparable runs

CodeNinja 1.0 OpenChat 7B i1 needs ~12.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~19 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) — 12.9 GB, 18.6 tok/s, Runs well

12.9 GB required46.1 GB available

28% VRAM used

Fit status

Runs well

Decode

18.6 tok/s

TTFT

10400 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 feelsCodeNinja 1.0 OpenChat 7B i1 on Mac mini M4 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 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	18.6 tok/s	5673 ms	663K
Coding	C	Runs well	18.6 tok/s	10400 ms	663K
Agentic Coding	C	Runs well	18.6 tok/s	15127 ms	663K
Reasoning	C	Runs well	18.6 tok/s	12291 ms	663K
RAG	C	Runs well	18.6 tok/s	18909 ms	663K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Mac mini M4 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

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 372%.87.8 tok/s decode

Raises estimated decode speed by about 372%.

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 202%.56.2 tok/s decode

Raises estimated decode speed by about 202%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 427%.98 tok/s decode

Raises estimated decode speed by about 427%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, Mac mini M4 64GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 18.6 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on Mac mini M4 64GB?

On Mac mini M4 64GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10400ms using Q4_K_M quantization.

Can Mac mini M4 64GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Mac mini M4 64GB receives a C grade with 18.6 tok/s and 663K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on Mac mini M4 64GB?

On Mac mini M4 64GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 663K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac mini M4 64GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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