Can granite 8b code instruct 4k run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable

Estimated from fit model

granite 8b code instruct 4k needs ~17.1 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~48 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) — 17.1 GB, 47.5 tok/s, Runs well

17.1 GB required69.1 GB available

25% VRAM used

Fit status

Runs well

Decode

47.5 tok/s

TTFT

4072 ms

Safe context

904K

Memory

17.1 GB / 69.1 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on MacBook Pro M2 Max 96GB
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: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 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	47.5 tok/s	2221 ms	904K
Coding	C	Runs well	47.5 tok/s	4072 ms	904K
Agentic Coding	C	Runs well	47.5 tok/s	5923 ms	904K
Reasoning	C	Runs well	47.5 tok/s	4813 ms	904K
RAG	C	Runs well	47.5 tok/s	7404 ms	904K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C40
Q3_K_S	3	3.9 GB	Low	C40
NVFP4	4	4.5 GB	Medium	C40
Q4_K_M	4	4.9 GB	Medium	C40
Q5_K_M	5	5.8 GB	High	C40
Q6_K	6	6.6 GB	High	C40
Q8_0	8	8.6 GB	Very High	C41
F16Best for your GPU	16	16.4 GB	Maximum	C42

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien granite 8b code instruct 4k

Mac Studio M2 Ultra 128GBOpción económica

128 GB Unified (+32)800 GB/s (+400)

Sube la velocidad estimada de decodificación alrededor de un 100%.95.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 100%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBMejor relación calidad-precio

128 GB Unified (+32)800 GB/s (+400)

Sube la velocidad estimada de decodificación alrededor de un 90%.90.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 90%.

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

~$3,999 MSRP

MacBook Pro M4 Max 128GBMejora Apple

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

Sube la velocidad estimada de decodificación alrededor de un 62%.76.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 62%.

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

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run granite 8b code instruct 4k?

Yes, MacBook Pro M2 Max 96GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 47.5 tok/s.

How much VRAM does granite 8b code instruct 4k need?

granite 8b code instruct 4k (8B parameters) requires approximately 17.1 GB of memory with Q4_K_M quantization.

What is the best quantization for granite 8b code instruct 4k?

The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.

What speed will granite 8b code instruct 4k run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, granite 8b code instruct 4k achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4072ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Pro M2 Max 96GB receives a C grade with 47.5 tok/s and 904K context.

What context window can granite 8b code instruct 4k use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, granite 8b code instruct 4k can safely use up to 904K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for granite 8b code instruct 4k?

Not always. MacBook Pro M2 Max 96GB 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.

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