Can CodeGeeX 4 9B run on MacBook Air M2 16GB?

YES — Runs Great

A78Great

Estimated from fit model

CodeGeeX 4 9B needs ~8.7 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~13 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) — 8.7 GB, 12.9 tok/s, Runs well

8.7 GB required11.5 GB available

76% VRAM used

Fit status

Runs well

Decode

12.9 tok/s

TTFT

14950 ms

Safe context

89K

Memory

8.7 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on MacBook Air M2 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: 12.9 tok/s decode · 14.9s TTFT (warm) · 32 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	12.9 tok/s	8155 ms	89K
Coding	A	Runs well	12.9 tok/s	14950 ms	89K
Agentic Coding	A	Runs well	12.9 tok/s	21746 ms	89K
Reasoning	A	Runs well	12.9 tok/s	17668 ms	89K
RAG	A	Runs well	12.9 tok/s	27182 ms	89K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A78
Q3_K_S	3	4.4 GB	Low	A79
NVFP4	4	5.0 GB	Medium	A80
Q4_K_M	4	5.5 GB	Medium	A80
Q5_K_M	5	6.5 GB	High	A80
Q6_KBest for your GPU	6	7.4 GB	High	A80
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run CodeGeeX 4 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "THUDM/codegeex4-all-9b" \
  --hf-file "codegeex4-all-9b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Air M2 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	6.4 tok/s
Ministral 3 14B	14B	B	6.4 tok/s

Frequently asked questions

Can MacBook Air M2 16GB run CodeGeeX 4 9B?

Yes, MacBook Air M2 16GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 12.9 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeGeeX 4 9B?

The recommended quantization for CodeGeeX 4 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeGeeX 4 9B run at on MacBook Air M2 16GB?

On MacBook Air M2 16GB, CodeGeeX 4 9B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 14950ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on MacBook Air M2 16GB receives a A grade with 12.9 tok/s and 89K context.

What context window can CodeGeeX 4 9B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, CodeGeeX 4 9B can safely use up to 89K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for CodeGeeX 4 9B?

Not always. MacBook Air M2 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.

See all results for MacBook Air M2 16GB See all hardware for CodeGeeX 4 9B

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