Can granite 8b code instruct 4k run on MacBook Pro M3 Max 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

granite 8b code instruct 4k needs ~11.9 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~49 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) — 11.9 GB, 49.2 tok/s, Runs well

11.9 GB required34.6 GB available

34% VRAM used

Fit status

Runs well

Decode

49.2 tok/s

TTFT

3937 ms

Safe context

403K

Memory

11.9 GB / 34.6 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on MacBook Pro M3 Max 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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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	49.2 tok/s	2147 ms	403K
Coding	C	Runs well	49.2 tok/s	3937 ms	403K
Agentic Coding	C	Runs well	49.2 tok/s	5726 ms	403K
Reasoning	C	Runs well	49.2 tok/s	4652 ms	403K
RAG	C	Runs well	49.2 tok/s	7157 ms	403K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C43
Q3_K_S	3	3.9 GB	Low	C43
NVFP4	4	4.5 GB	Medium	C43
Q4_K_M	4	4.9 GB	Medium	C43
Q5_K_M	5	5.8 GB	High	C43
Q6_K	6	6.6 GB	High	C44
Q8_0	8	8.6 GB	Very High	C44
F16Best for your GPU	16	16.4 GB	Maximum	C48

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

Upgrade-Optionen

Hardware, die granite 8b code instruct 4k gut ausführt

Mac Studio M2 Ultra 64GBGünstige Wahl

64 GB Unified (+16)800 GB/s (+400)

Raises estimated decode speed by about 93%.95.1 tok/s Dekodierung

Raises estimated decode speed by about 93%.

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

ca. $3,999 MSRP

Mac Studio M1 Ultra 64GBBestes Preis-Leistungs-Verhältnis

64 GB Unified (+16)800 GB/s (+400)

Raises estimated decode speed by about 83%.90.2 tok/s Dekodierung

Raises estimated decode speed by about 83%.

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

ca. $3,999 MSRP

MacBook Pro M4 Max 64GBApple-Upgrade

64 GB Unified (+16)546 GB/s (+146)

Raises estimated decode speed by about 56%.76.8 tok/s Dekodierung

Raises estimated decode speed by about 56%.

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

ca. $3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 48GB run granite 8b code instruct 4k?

Yes, MacBook Pro M3 Max 48GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 49.2 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 11.9 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 M3 Max 48GB?

On MacBook Pro M3 Max 48GB, granite 8b code instruct 4k achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3937ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 48GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Pro M3 Max 48GB receives a C grade with 49.2 tok/s and 403K context.

What context window can granite 8b code instruct 4k use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, granite 8b code instruct 4k can safely use up to 403K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for granite 8b code instruct 4k?

Not always. MacBook Pro M3 Max 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.

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