Can CodeGeeX 4 9B run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

A78Great

○Estimated from fit model

CodeGeeX 4 9B needs ~9.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.7 GB, 112.5 tok/s, Runs well

9.7 GB required24.0 GB available

40% VRAM used

Fit status

Runs well

Decode

112.5 tok/s

TTFT

1722 ms

Safe context

131K

Memory

9.7 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX PRO 4000 Blackwell 24GB
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: 112.5 tok/s decode · 1.7s TTFT (warm) · 281 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	102.8 tok/s	1027 ms	131K
Coding	A	Runs well	102.8 tok/s	1883 ms	131K
Agentic Coding	A	Runs well	102.8 tok/s	2739 ms	131K
Reasoning	A	Runs well	102.8 tok/s	2225 ms	131K
RAG	A	Runs well	102.8 tok/s	3423 ms	131K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A73
Q3_K_S	3	4.4 GB	Low	A73
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 PRO 4000 Blackwell 24GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	85.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	37 tok/s

Frequently asked questions

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for CodeGeeX 4 9B

URL: https://willitrunai.com/can-run/codegeex-4-9b-on-rtx-pro-4000-blackwell-24gb

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