Can CodeGeeX 4 9B run on NVIDIA V100 32GB?

YES — Runs Great

A77Great

Estimated from fit model

CodeGeeX 4 9B needs ~10.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~110 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 10.5 GB, 120.1 tok/s, Runs well

10.5 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

120.1 tok/s

TTFT

1612 ms

Safe context

131K

Memory

10.5 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on NVIDIA V100 32GB
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: 120.1 tok/s decode · 1.6s TTFT (warm) · 300 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	120.1 tok/s	879 ms	131K
Coding	A	Runs well	109.8 tok/s	1763 ms	131K
Agentic Coding	A	Runs well	120.1 tok/s	2344 ms	131K
Reasoning	A	Runs well	120.1 tok/s	1905 ms	131K
RAG	A	Runs well	120.1 tok/s	2930 ms	131K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A71
Q3_K_S	3	4.4 GB	Low	A71
NVFP4	4	5.0 GB	Medium	A71
Q4_K_M	4	5.5 GB	Medium	A72
Q5_K_M	5	6.5 GB	High	A72
Q6_K	6	7.4 GB	High	A72
Q8_0	8	9.6 GB	Very High	A73
F16Best for your GPU	16	18.5 GB	Maximum	A77

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 NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run CodeGeeX 4 9B?

Yes, NVIDIA V100 32GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 109.8 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 10.5 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, CodeGeeX 4 9B achieves approximately 109.8 tokens per second decode speed with a time-to-first-token of 1763ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on NVIDIA V100 32GB receives a A grade with 109.8 tok/s and 131K context.

What context window can CodeGeeX 4 9B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, 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 NVIDIA V100 32GB See all hardware for CodeGeeX 4 9B

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