Can CodeGeeX 4 9B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A70Great

Estimated from fit model

CodeGeeX 4 9B needs ~20.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~33 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) — 20.4 GB, 32.6 tok/s, Runs well

20.4 GB required108.8 GB available

19% VRAM used

Fit status

Runs well

Decode

32.6 tok/s

TTFT

5933 ms

Safe context

131K

Memory

20.4 GB / 108.8 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on NVIDIA DGX Spark 128GB
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: 32.6 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	32.6 tok/s	3236 ms	131K
Coding	A	Runs well	32.6 tok/s	5933 ms	131K
Agentic Coding	A	Runs well	32.6 tok/s	8629 ms	131K
Reasoning	A	Runs well	32.6 tok/s	7011 ms	131K
RAG	A	Runs well	32.6 tok/s	10787 ms	131K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B67
Q3_K_S	3	4.4 GB	Low	B67
NVFP4	4	5.0 GB	Medium	B67
Q4_K_M	4	5.5 GB	Medium	B67
Q5_K_M	5	6.5 GB	High	B67
Q6_K	6	7.4 GB	High	B67
Q8_0	8	9.6 GB	Very High	B67
F16Best for your GPU	16	18.5 GB	Maximum	B68

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 DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	24.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s
Qwen 3.6 27B	27B	A	10.8 tok/s
Qwen 3.5 122B A10B	122B	S	6.6 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run CodeGeeX 4 9B?

Yes, NVIDIA DGX Spark 128GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 32.6 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 20.4 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 DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, CodeGeeX 4 9B achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5933ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on NVIDIA DGX Spark 128GB receives a A grade with 32.6 tok/s and 131K context.

What context window can CodeGeeX 4 9B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, 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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for CodeGeeX 4 9B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for CodeGeeX 4 9B

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