Can CodeGeeX 4 9B run on RTX 2060 Super 8GB?

YES — With Offload

A80Great

○Estimated from fit model

CodeGeeX 4 9B needs ~7.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 7.8 GB, 51.8 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

51.8 tok/s

TTFT

3740 ms

Safe context

21K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX 2060 Super 8GB
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: 51.8 tok/s decode · 3.7s TTFT (warm) · 129 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	51.8 tok/s	2040 ms	21K
Coding	A	Runs with offload	51.8 tok/s	3740 ms	21K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	33.8 tok/s	8324 ms	21K
Reasoning	A	Runs with offload	51.8 tok/s	4420 ms	21K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	33.8 tok/s	10406 ms	21K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A81
Q3_K_S	3	4.4 GB	Low	A81
NVFP4Best for your GPU	4	5.0 GB	Medium	A81
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
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 2060 Super 8GB See all hardware for CodeGeeX 4 9B

URL: https://willitrunai.com/can-run/codegeex-4-9b-on-rtx-2060-super-8gb