Can CodeGeeX 4 9B run on Radeon RX 6850M XT 12GB?

YES — Runs Great

A82Great

○Estimated from fit model

CodeGeeX 4 9B needs ~8.2 GB VRAM. Radeon RX 6850M XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 8.2 GB, 48.2 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

48.2 tok/s

TTFT

4017 ms

Safe context

116K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on Radeon RX 6850M XT 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: 48.2 tok/s decode · 4.0s TTFT (warm) · 121 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	48.2 tok/s	2191 ms	116K
Coding	A	Runs well	48.2 tok/s	4017 ms	116K
Agentic Coding	A	Runs well	48.2 tok/s	5843 ms	116K
Reasoning	A	Runs well	48.2 tok/s	4747 ms	116K
RAG	A	Runs well	48.2 tok/s	7303 ms	116K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on Radeon RX 6850M XT 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	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 Radeon RX 6850M XT 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	A	19.1 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	A	15.4 tok/s
👁 Mistral Ministral 3 14B	14B	A	19 tok/s
👁 Microsoft Phi-4 14B	14B	A	17.3 tok/s
👁 Alibaba Qwen 2.5 14B	14B	B	17.7 tok/s

Frequently asked questions

See all results for Radeon RX 6850M XT 12GB See all hardware for CodeGeeX 4 9B

URL: https://willitrunai.com/can-run/codegeex-4-9b-on-rx-6850m-xt-12gb

⇱ CodeGeeX 4 9B on Radeon RX 6850M XT 12GB? YES