Is unified memory on MacBook Pro M4 32GB as fast as VRAM for granite 8b code instruct 4k?

Not always. MacBook Pro M4 32GB 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 32GB?

YES — Runs Great

C46Usable

Estimated — low-sample bucket· few comparable runs

granite 8b code instruct 4k needs ~10.2 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 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) — 10.2 GB, 16.3 tok/s, Runs well

10.2 GB required23.0 GB available

44% VRAM used

Fit status

Runs well

Decode

16.3 tok/s

TTFT

11886 ms

Safe context

236K

Memory

10.2 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on MacBook Pro M4 32GB
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: 16.3 tok/s decode · 11.9s TTFT (warm) · 41 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	16.3 tok/s	6483 ms	236K
Coding	C	Runs well	16.3 tok/s	11886 ms	236K
Agentic Coding	C	Runs well	16.3 tok/s	17288 ms	236K
Reasoning	C	Runs well	16.3 tok/s	14047 ms	236K
RAG	C	Runs well	16.3 tok/s	21610 ms	236K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
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 M3 Pro 36GBBudget pick

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 37%.22.4 tok/s decode

Raises estimated decode speed by about 37%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 254%.57.7 tok/s decode

Raises estimated decode speed by about 254%.

~$2,499 MSRP

MacBook Pro M4 Max 48GBApple upgrade

48 GB Unified (+16)546 GB/s (+426)

Raises estimated decode speed by about 371%.76.8 tok/s decode

Raises estimated decode speed by about 371%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M4 32GB run granite 8b code instruct 4k?

Yes, MacBook Pro M4 32GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 16.3 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 10.2 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 32GB?

On MacBook Pro M4 32GB, granite 8b code instruct 4k achieves approximately 16.3 tokens per second decode speed with a time-to-first-token of 11886ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Pro M4 32GB receives a C grade with 16.3 tok/s and 236K context.

What context window can granite 8b code instruct 4k use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, granite 8b code instruct 4k can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 32GB See all hardware for granite 8b code instruct 4k

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