Is unified memory on MacBook Air M1 16GB as fast as VRAM for granite 8b code instruct 4k?

Not always. MacBook Air M1 16GB 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 Air M1 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

granite 8b code instruct 4k needs ~8.4 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~8 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) — 8.4 GB, 8.4 tok/s, Runs well

8.4 GB required11.5 GB available

73% VRAM used

Fit status

Runs well

Decode

8.4 tok/s

TTFT

23156 ms

Safe context

68K

Memory

8.4 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on MacBook Air M1 16GB
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: 8.4 tok/s decode · 23.2s TTFT (warm) · 21 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	8.4 tok/s	12631 ms	68K
Coding	C	Runs well	8.4 tok/s	23156 ms	68K
Agentic Coding	C	Runs well	8.4 tok/s	33682 ms	68K
Reasoning	C	Runs well	8.4 tok/s	27366 ms	68K
RAG	C	Runs well	8.4 tok/s	42102 ms	68K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C51
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 18GBBudget pick

18 GB Unified (+2)150 GB/s (+82)

Raises estimated decode speed by about 167%.22.4 tok/s decode

Raises estimated decode speed by about 167%.

~$1,999 MSRP

MacBook Pro M4 Pro 24GBBest value

24 GB Unified (+8)273 GB/s (+205)

Raises estimated decode speed by about 371%.39.6 tok/s decode

Raises estimated decode speed by about 371%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M1 16GB run granite 8b code instruct 4k?

Yes, MacBook Air M1 16GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 8.4 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 8.4 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 M1 16GB?

On MacBook Air M1 16GB, granite 8b code instruct 4k achieves approximately 8.4 tokens per second decode speed with a time-to-first-token of 23156ms using Q4_K_M quantization.

Can MacBook Air M1 16GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on MacBook Air M1 16GB receives a C grade with 8.4 tok/s and 68K context.

What context window can granite 8b code instruct 4k use on MacBook Air M1 16GB?

On MacBook Air M1 16GB, granite 8b code instruct 4k can safely use up to 68K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Air M1 16GB See all hardware for granite 8b code instruct 4k

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