Can CodeNinja 1.0 OpenChat 7B i1 run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C46Usable

Estimated — low-sample bucket· few comparable runs

CodeNinja 1.0 OpenChat 7B i1 needs ~11.2 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~45 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) — 11.2 GB, 45.3 tok/s, Runs well

11.2 GB required34.6 GB available

32% VRAM used

Fit status

Runs well

Decode

45.3 tok/s

TTFT

4275 ms

Safe context

472K

Memory

11.2 GB / 34.6 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M4 Pro 48GB
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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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	45.3 tok/s	2332 ms	472K
Coding	C	Runs well	45.3 tok/s	4275 ms	472K
Agentic Coding	C	Runs well	45.3 tok/s	6218 ms	472K
Reasoning	C	Runs well	45.3 tok/s	5052 ms	472K
RAG	C	Runs well	45.3 tok/s	7772 ms	472K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C42
NVFP4	4	3.9 GB	Medium	C42
Q4_K_M	4	4.3 GB	Medium	C43
Q5_K_M	5	5.0 GB	High	C43
Q6_K	6	5.7 GB	High	C43
Q8_0	8	7.5 GB	Very High	C44
F16Best for your GPU	16	14.3 GB	Maximum	C47

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

Opciones de mejora

Hardware que ejecuta bien CodeNinja 1.0 OpenChat 7B i1

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+48)546 GB/s (+273)

Sube la velocidad estimada de decodificación alrededor de un 94%.87.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 94%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M2 Ultra 64GBMejor relación calidad-precio

64 GB Unified (+16)800 GB/s (+527)

Sube la velocidad estimada de decodificación alrededor de un 116%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 116%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Mac Studio M1 Ultra 64GBMejora Apple

64 GB Unified (+16)800 GB/s (+527)

Sube la velocidad estimada de decodificación alrededor de un 116%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 116%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, MacBook Pro M4 Pro 48GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 45.3 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 11.2 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 M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M4 Pro 48GB receives a C grade with 45.3 tok/s and 472K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 472K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for CodeNinja 1.0 OpenChat 7B i1?

Not always. MacBook Pro M4 Pro 48GB 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 M4 Pro 48GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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