Can CodeGeeX 4 9B run on RX 5600 XT 6GB?

Q: What should I upgrade first if CodeGeeX 4 9B feels slow on RX 5600 XT 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

B60Good

Estimated from fit model

CodeGeeX 4 9B needs ~7.2 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With NVFP4 quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

CodeGeeX 4 9B at Q4_K_M needs 7.6 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at NVFP4 (7.2 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.6 GB, exceeds 6.0 GB available

7.6 GB required6.0 GB available

127% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.6 tok/s

TTFT

14231 ms

Safe context

Memory

7.6 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeGeeX 4 9B on RX 5600 XT 6GB
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: 13.6 tok/s decode · 14.2s TTFT (warm) · 34 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.8 tok/s	7121 ms	4K
Coding	F	Too heavy	13.6 tok/s	14231 ms	4K
Agentic Coding	F	Too heavy	11.6 tok/s	24355 ms	4K
Reasoning	F	Too heavy	13.6 tok/s	16819 ms	4K
RAG	F	Too heavy	11.6 tok/s	30444 ms	4K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	A81
Q3_K_S	3	4.4 GB	Low	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

Upgrade options

Hardware that runs CodeGeeX 4 9B well

RX 580 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.36.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.33.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$269 MSRP

RTX 3080 10GBBiggest leap

10 GB VRAM (+4)760 GB/s (+472)

Makes the model fit on the accelerator instead of staying completely out of reach.96.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$699 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run CodeGeeX 4 9B?

Yes, RX 5600 XT 6GB can run CodeGeeX 4 9B at NVFP4 quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 7.6 GB which exceeds available memory, but at NVFP4 it needs only 7.2 GB. Expected decode speed: 17.7 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 7.6 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at NVFP4 using 7.2 GB.

What is the best quantization for CodeGeeX 4 9B?

The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is NVFP4, which uses 7.2 GB.

What speed will CodeGeeX 4 9B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, CodeGeeX 4 9B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10943ms using NVFP4 quantization.

Can RX 5600 XT 6GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RX 5600 XT 6GB receives a F grade with 13.6 tok/s and 4K context.

What context window can CodeGeeX 4 9B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, CodeGeeX 4 9B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 5600 XT 6GB See all hardware for CodeGeeX 4 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/codegeex-4-9b-on-rx-5600-xt-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: