Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for granite 8b code instruct 4k?

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.

Can granite 8b code instruct 4k run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C46Usable

Estimated — low-sample bucket· few comparable runs

granite 8b code instruct 4k needs ~11.9 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~40 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, 39.6 tok/s, Runs well

11.9 GB required34.6 GB available

34% VRAM used

Fit status

Runs well

Decode

39.6 tok/s

TTFT

4885 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 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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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	39.6 tok/s	2665 ms	403K
Coding	C	Runs well	39.6 tok/s	4885 ms	403K
Agentic Coding	C	Runs well	39.6 tok/s	7106 ms	403K
Reasoning	C	Runs well	39.6 tok/s	5773 ms	403K
RAG	C	Runs well	39.6 tok/s	8882 ms	403K

Inference speed

granite 8b code instruct 4k inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for granite 8b code instruct 4k at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M4 Pro 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

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 options

Hardware that runs granite 8b code instruct 4k well

MacBook Pro M4 Max 96GBBudget pick

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

Raises estimated decode speed by about 94%.76.8 tok/s decode

Raises estimated decode speed by about 94%.

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

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

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

Raises estimated decode speed by about 140%.95.1 tok/s decode

Raises estimated decode speed by about 140%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 64GBApple upgrade

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

Raises estimated decode speed by about 128%.90.2 tok/s decode

Raises estimated decode speed by about 128%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run granite 8b code instruct 4k?

Yes, MacBook Pro M4 Pro 48GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 39.6 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 M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, granite 8b code instruct 4k achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4885ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Pro M4 Pro 48GB receives a C grade with 39.6 tok/s and 403K context.

What context window can granite 8b code instruct 4k use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 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.

See all results for MacBook Pro M4 Pro 48GB See all hardware for granite 8b code instruct 4k

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