Would CUDA be a better path than Intel Arc B580 12GB for CodeGeeX 4 9B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can CodeGeeX 4 9B run on Intel Arc B580 12GB?

Q: What should I upgrade first if CodeGeeX 4 9B feels slow on Intel Arc B580 12GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — Runs Great

A82Great

Estimated from fit model

CodeGeeX 4 9B needs ~8.2 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.2 GB, 43.6 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

43.6 tok/s

TTFT

4440 ms

Safe context

116K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on Intel Arc B580 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: 43.6 tok/s decode · 4.4s TTFT (warm) · 109 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	39.9 tok/s	2649 ms	116K
Coding	A	Runs well	39.9 tok/s	4856 ms	116K
Agentic Coding	A	Runs well	39.9 tok/s	7063 ms	116K
Reasoning	A	Runs well	39.9 tok/s	5739 ms	116K
RAG	A	Runs well	39.9 tok/s	8829 ms	116K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on Intel Arc B580 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

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 Intel Arc B580 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	17.8 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	14.4 tok/s

Frequently asked questions

Can Intel Arc B580 12GB run CodeGeeX 4 9B?

Yes, Intel Arc B580 12GB can run CodeGeeX 4 9B with a A grade (Runs well). Expected decode speed: 39.9 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 8.2 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 Intel Arc B580 12GB?

On Intel Arc B580 12GB, CodeGeeX 4 9B achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on Intel Arc B580 12GB receives a A grade with 39.9 tok/s and 116K context.

What context window can CodeGeeX 4 9B use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, CodeGeeX 4 9B can safely use up to 116K 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 Intel Arc B580 12GB?

See all results for Intel Arc B580 12GB See all hardware for CodeGeeX 4 9B

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