Can Granite Code 8B run on MacBook Air M1 16GB?

YES — Tight Fit

A72Great

○Estimated from fit model

Granite Code 8B needs ~9.5 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 9.0 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

9.0 tok/s

TTFT

21541 ms

Safe context

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGranite Code 8B 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: 9.0 tok/s decode · 21.5s TTFT (warm) · 23 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	A	Runs well	9.0 tok/s	11749 ms	8K
Coding	A	Tight fit	9.0 tok/s	21541 ms	8K
Agentic Coding	A	Runs with offload	9.0 tok/s	31332 ms	8K
Reasoning	A	Tight fit	9.0 tok/s	25457 ms	8K
RAG	A	Runs with offload	9.0 tok/s	39165 ms	8K

Quantization options

How Granite Code 8B (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	A75
Q3_K_S	3	3.9 GB	Low	A76
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A78
Q6_KBest for your GPU	6	6.6 GB	High	A77
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 8B on your machine.

Run

ollama run granite-code:8b

Your hardware

More models your MacBook Air M1 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	8 tok/s
👁 Alibaba Qwen 3 14B	14B	B	4 tok/s
👁 Mistral Ministral 3 14B	14B	B	4 tok/s
👁 NVIDIA Nemotron Nano 9B v2	9B	A	8 tok/s
👁 Tsinghua/Zhipu CodeGeeX 4 9B	9B	A	8.1 tok/s

Frequently asked questions

See all results for MacBook Air M1 16GB See all hardware for Granite Code 8B

URL: https://willitrunai.com/can-run/granite-code-8b-on-m1-16gb

⇱ Granite Code 8B on MacBook Air M1 16GB? TIGHT FIT