Can granite 8b code instruct 4k run on MacBook Air M3 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

granite 8b code instruct 4k needs ~9.3 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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) — 9.3 GB, 13.9 tok/s, Runs well

9.3 GB required17.3 GB available

54% VRAM used

Fit status

Runs well

Decode

13.9 tok/s

TTFT

13894 ms

Safe context

152K

Memory

9.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on MacBook Air M3 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: 13.9 tok/s decode · 13.9s TTFT (warm) · 35 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	13.9 tok/s	7578 ms	152K
Coding	C	Runs well	13.9 tok/s	13894 ms	152K
Agentic Coding	C	Runs well	13.9 tok/s	20209 ms	152K
Reasoning	C	Runs well	13.9 tok/s	16420 ms	152K
RAG	C	Runs well	13.9 tok/s	25261 ms	152K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C47
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C48
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C49
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

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

MacBook Pro M2 Max 32GBOpción económica

32 GB Unified (+8)400 GB/s (+300)

Sube la velocidad estimada de decodificación alrededor de un 242%.47.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 242%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBMejor relación calidad-precio

32 GB Unified (+8)200 GB/s (+100)

Sube la velocidad estimada de decodificación alrededor de un 106%.28.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 106%.

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

~$1,999 MSRP

MacBook Pro M1 Pro 32GBMejora Apple

32 GB Unified (+8)200 GB/s (+100)

Sube la velocidad estimada de decodificación alrededor de un 91%.26.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run granite 8b code instruct 4k?

Yes, MacBook Air M3 24GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 13.9 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 9.3 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 Air M3 24GB?

On MacBook Air M3 24GB, granite 8b code instruct 4k achieves approximately 13.9 tokens per second decode speed with a time-to-first-token of 13894ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Air M3 24GB receives a C grade with 13.9 tok/s and 152K context.

What context window can granite 8b code instruct 4k use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, granite 8b code instruct 4k can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M3 24GB as fast as VRAM for granite 8b code instruct 4k?

Not always. MacBook Air M3 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.

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