Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for CodeNinja 1.0 OpenChat 7B i1?

Not always. MacBook Pro M2 Max 32GB 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 MacBook Pro M2 Max 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~9.4 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~54 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) — 9.4 GB, 54.3 tok/s, Runs well

9.4 GB required23.0 GB available

41% VRAM used

Fit status

Runs well

Decode

54.3 tok/s

TTFT

3563 ms

Safe context

281K

Memory

9.4 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M2 Max 32GB
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: 54.3 tok/s decode · 3.6s TTFT (warm) · 136 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	54.3 tok/s	1944 ms	281K
Coding	C	Runs well	54.3 tok/s	3563 ms	281K
Agentic Coding	C	Runs well	54.3 tok/s	5183 ms	281K
Reasoning	C	Runs well	54.3 tok/s	4211 ms	281K
RAG	C	Runs well	54.3 tok/s	6479 ms	281K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
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

RX 7900 XTX 24GBBest value

960 GB/s (+560)

Raises estimated decode speed by about 80%.98 tok/s decode

Raises estimated decode speed by about 80%.

~$999 MSRP

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+4)410 GB/s (+10)

This setup is broadly balanced for this model.65.9 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 32GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, MacBook Pro M2 Max 32GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 54.3 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 9.4 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 MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M2 Max 32GB receives a C grade with 54.3 tok/s and 281K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Max 32GB 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-m2-max-32gb" 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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