Can CodeGeeX 4 9B run on RTX 2080 Ti 11GB?

YES — Runs Great

A84Great

○Estimated from fit model

CodeGeeX 4 9B needs ~8.4 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~80 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) — 8.4 GB, 79.8 tok/s, Runs well

8.4 GB required11.0 GB available

76% VRAM used

Fit status

Runs well

Decode

79.8 tok/s

TTFT

2427 ms

Safe context

84K

Memory

8.4 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX 2080 Ti 11GB
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: 79.8 tok/s decode · 2.4s TTFT (warm) · 199 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	79.8 tok/s	1324 ms	84K
Coding	A	Runs well	79.8 tok/s	2427 ms	84K
Agentic Coding	A	Runs well	79.8 tok/s	3530 ms	84K
Reasoning	A	Runs well	79.8 tok/s	2868 ms	84K
RAG	A	Runs well	79.8 tok/s	4413 ms	84K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A79
Q3_K_S	3	4.4 GB	Low	A80
NVFP4	4	5.0 GB	Medium	A81
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

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for CodeGeeX 4 9B

URL: https://willitrunai.com/can-run/codegeex-4-9b-on-rtx-2080-ti-11gb