Can CodeGeeX 4 9B run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

A83Great

Estimated from fit model

CodeGeeX 4 9B needs ~8.5 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.5 GB, 62.8 tok/s, Runs well

8.5 GB required12.0 GB available

71% VRAM used

Fit status

Runs well

Decode

62.8 tok/s

TTFT

3081 ms

Safe context

108K

Memory

8.5 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX 4000 Ada Laptop 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: 62.8 tok/s decode · 3.1s TTFT (warm) · 157 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	62.8 tok/s	1681 ms	108K
Coding	A	Runs well	62.8 tok/s	3081 ms	108K
Agentic Coding	A	Runs well	62.8 tok/s	4482 ms	108K
Reasoning	A	Runs well	62.8 tok/s	3642 ms	108K
RAG	A	Runs well	62.8 tok/s	5603 ms	108K

Inference speed

CodeGeeX 4 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeGeeX 4 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	121.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	111.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	92.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	87.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	75.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	74.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	74.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	47.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	38.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	36.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 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 RTX 4000 Ada Laptop 12GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	A	23.8 tok/s
Ministral 3 14B	14B	A	23.7 tok/s
Phi-4 14B	14B	A	21.5 tok/s
Qwen 2.5 14B	14B	A	22.1 tok/s

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B?

Yes, RTX 4000 Ada Laptop 12GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 62.8 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.5 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 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B achieves approximately 62.8 tokens per second decode speed with a time-to-first-token of 3081ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX 4000 Ada Laptop 12GB receives a A grade with 62.8 tok/s and 108K context.

What context window can CodeGeeX 4 9B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B can safely use up to 108K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for CodeGeeX 4 9B

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

62.8 tok/s

1681 ms

108K

Coding

Runs well

62.8 tok/s

3081 ms

108K

Agentic Coding

Runs well

62.8 tok/s

4482 ms

108K

Reasoning

Runs well

62.8 tok/s

3642 ms

108K

RAG

Runs well

62.8 tok/s

5603 ms

108K

CodeGeeX 4 9B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

126.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

126.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

121.7

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

111.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

92.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

87.7

Fits

RTX 4070 12GB

12 GB

Q4_K_M

75.3

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

74.7

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

74.7

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.8

Fits

RTX 3060 12GB

12 GB

Q4_K_M

47.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.8

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

38.5

Fits

RTX 4060 8GB

8 GB

Q4_K_M

36.4

Offloads

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 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

Model

Params

Grade

Decode

Capabilities

Qwen 3 14B

14B

23.8 tok/s

Ministral 3 14B

14B

23.7 tok/s

Phi-4 14B

14B

21.5 tok/s

Qwen 2.5 14B

14B

22.1 tok/s

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B?

Yes, RTX 4000 Ada Laptop 12GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 62.8 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.5 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 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B achieves approximately 62.8 tokens per second decode speed with a time-to-first-token of 3081ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX 4000 Ada Laptop 12GB receives a A grade with 62.8 tok/s and 108K context.

What context window can CodeGeeX 4 9B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, CodeGeeX 4 9B can safely use up to 108K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.