Can CodeGeeX 4 9B run on RTX 4060 8GB?

YES — With Offload

A79Great

Estimated from fit model

CodeGeeX 4 9B needs ~7.8 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 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, 36.4 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

36.4 tok/s

TTFT

5320 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 4060 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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 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.

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	36.2 tok/s	2920 ms	21K
Coding	A	Runs with offload	36.2 tok/s	5353 ms	21K
Agentic Coding	A	Runs with offload	24.4 tok/s	11535 ms	21K
Reasoning	A	Runs with offload	36.2 tok/s	6326 ms	21K
RAG	A	Runs with offload	24.4 tok/s	14419 ms	21K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 4060 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

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

Can RTX 4060 8GB run CodeGeeX 4 9B?

Yes, RTX 4060 8GB can run CodeGeeX 4 9B with a A grade (Runs with offload). Expected decode speed: 36.2 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 7.8 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 4060 8GB?

On RTX 4060 8GB, CodeGeeX 4 9B achieves approximately 36.2 tokens per second decode speed with a time-to-first-token of 5353ms using Q4_K_M quantization.

Can RTX 4060 8GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX 4060 8GB receives a A grade with 36.2 tok/s and 21K context.

What context window can CodeGeeX 4 9B use on RTX 4060 8GB?

On RTX 4060 8GB, CodeGeeX 4 9B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if CodeGeeX 4 9B feels slow on RTX 4060 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4060 8GB See all hardware for CodeGeeX 4 9B

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