Can CodeGeeX 4 9B run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

A80Great

Estimated from fit model

CodeGeeX 4 9B needs ~8.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~22 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) — 8.9 GB, 21.8 tok/s, Runs well

8.9 GB required13.0 GB available

68% VRAM used

Fit status

Runs well

Decode

21.8 tok/s

TTFT

8875 ms

Safe context

121K

Memory

8.9 GB / 13.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on MacBook Pro M3 Pro 18GB
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: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 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	21.8 tok/s	4841 ms	121K
Coding	A	Runs well	21.8 tok/s	8875 ms	121K
Agentic Coding	A	Runs well	21.8 tok/s	12908 ms	121K
Reasoning	A	Runs well	21.8 tok/s	10488 ms	121K
RAG	A	Runs well	21.8 tok/s	16136 ms	121K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A77
Q3_K_S	3	4.4 GB	Low	A78
NVFP4	4	5.0 GB	Medium	A79
Q4_K_M	4	5.5 GB	Medium	A79
Q5_K_M	5	6.5 GB	High	A80
Q6_K	6	7.4 GB	High	A80
Q8_0Best for your GPU	8	9.6 GB	Very High	A79
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 Pro M3 Pro 18GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	A	12.3 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	10.6 tok/s
Ministral 3 14B	14B	A	12.3 tok/s
Phi-4 14B	14B	B	11.6 tok/s
Qwen 2.5 14B	14B	B	11.7 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run CodeGeeX 4 9B?

Yes, MacBook Pro M3 Pro 18GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 21.8 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.9 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 Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, CodeGeeX 4 9B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8875ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 18GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on MacBook Pro M3 Pro 18GB receives a A grade with 21.8 tok/s and 121K context.

What context window can CodeGeeX 4 9B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, CodeGeeX 4 9B can safely use up to 121K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for CodeGeeX 4 9B?

Not always. MacBook Pro M3 Pro 18GB 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 Pro M3 Pro 18GB See all hardware for CodeGeeX 4 9B

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