Can CodeGeeX 4 9B run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

A83Great

Estimated from fit model

CodeGeeX 4 9B needs ~8.5 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

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) — 8.5 GB, 62.8 tok/s, Runs well

8.5 GB required12.0 GB available

71% VRAM used

Fit status

Runs well

Decode

62.8 tok/s

TTFT

3081 ms

Safe context

108K

Memory

8.5 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX 4000 Ada Laptop 12GB
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: 62.8 tok/s decode · 3.1s TTFT (warm) · 157 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	62.8 tok/s	1681 ms	108K
Coding	A	Runs well	57.4 tok/s	3370 ms	108K
Agentic Coding	A	Runs well	62.8 tok/s	4482 ms	108K
Reasoning	A	Runs well	62.8 tok/s	3642 ms	108K
RAG	A	Runs well	62.8 tok/s	5603 ms	108K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 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

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 RTX 4000 Ada Laptop 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	23.8 tok/s
Ministral 3 14B	14B	A	23.7 tok/s

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B?

Yes, RTX 4000 Ada Laptop 12GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 57.4 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.5 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 RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3370ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX 4000 Ada Laptop 12GB receives a A grade with 57.4 tok/s and 108K context.

What context window can CodeGeeX 4 9B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B can safely use up to 108K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for CodeGeeX 4 9B

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