Can CodeGeeX 4 9B run on RTX A4500 20GB?

YES — Runs Great

A80Great

Estimated from fit model

CodeGeeX 4 9B needs ~9.3 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~100 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) — 9.3 GB, 99.5 tok/s, Runs well

9.3 GB required20.0 GB available

47% VRAM used

Fit status

Runs well

Decode

99.5 tok/s

TTFT

1947 ms

Safe context

131K

Memory

9.3 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX A4500 20GB
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: 99.5 tok/s decode · 1.9s TTFT (warm) · 249 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	99.5 tok/s	1062 ms	131K
Coding	A	Runs well	99.5 tok/s	1947 ms	131K
Agentic Coding	A	Runs well	99.5 tok/s	2831 ms	131K
Reasoning	A	Runs well	99.5 tok/s	2301 ms	131K
RAG	A	Runs well	99.5 tok/s	3539 ms	131K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A74
Q3_K_S	3	4.4 GB	Low	A74
NVFP4	4	5.0 GB	Medium	A75
Q4_K_M	4	5.5 GB	Medium	A75
Q5_K_M	5	6.5 GB	High	A76
Q6_K	6	7.4 GB	High	A76
Q8_0Best for your GPU	8	9.6 GB	Very High	A78
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 RTX A4500 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	41.2 tok/s
Qwen 3.5 27B	27B	A	18.6 tok/s
Qwen 3.6 27B	27B	S	23 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	43.8 tok/s
Magistral Small 2507	24B	S	26.7 tok/s

Frequently asked questions

Can RTX A4500 20GB run CodeGeeX 4 9B?

Yes, RTX A4500 20GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 99.5 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 9.3 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 A4500 20GB?

On RTX A4500 20GB, CodeGeeX 4 9B achieves approximately 99.5 tokens per second decode speed with a time-to-first-token of 1947ms using Q4_K_M quantization.

Can RTX A4500 20GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX A4500 20GB receives a A grade with 99.5 tok/s and 131K context.

What context window can CodeGeeX 4 9B use on RTX A4500 20GB?

On RTX A4500 20GB, CodeGeeX 4 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for CodeGeeX 4 9B

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