Can CodeGeeX 4 9B run on Intel Arc A580 8GB?

YES — With Offload

A80Great

Estimated from fit model

CodeGeeX 4 9B needs ~7.8 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 7.8 GB, 50.0 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

50.0 tok/s

TTFT

3873 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 Intel Arc A580 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: 50.0 tok/s decode · 3.9s TTFT (warm) · 125 tok/s prefill

What limits this setup

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

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.

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.

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	50.0 tok/s	2113 ms	21K
Coding	A	Runs with offload	50.0 tok/s	3873 ms	21K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	33.7 tok/s	8347 ms	21K
Reasoning	A	Runs with offload	50.0 tok/s	4578 ms	21K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	33.7 tok/s	10434 ms	21K

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on Intel Arc A580 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	4	5.0 GB	Medium	A81
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
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

Frequently asked questions

Can Intel Arc A580 8GB run CodeGeeX 4 9B?

Yes, Intel Arc A580 8GB can run CodeGeeX 4 9B with a A grade (Runs with offload). Expected decode speed: 50.0 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 Intel Arc A580 8GB?

On Intel Arc A580 8GB, CodeGeeX 4 9B achieves approximately 50.0 tokens per second decode speed with a time-to-first-token of 3873ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on Intel Arc A580 8GB receives a A grade with 50.0 tok/s and 21K context.

What context window can CodeGeeX 4 9B use on Intel Arc A580 8GB?

On Intel Arc A580 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 Intel Arc A580 8GB?

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.

Would CUDA be a better path than Intel Arc A580 8GB 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.

See all results for Intel Arc A580 8GB See all hardware for CodeGeeX 4 9B

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Preview:

What limits this setup

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

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.

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.

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	50.0 tok/s	2113 ms	21K
Coding	A	Runs with offload	50.0 tok/s	3873 ms	21K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	33.7 tok/s	8347 ms	21K
Reasoning	A	Runs with offload	50.0 tok/s	4578 ms	21K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	33.7 tok/s	10434 ms	21K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

A81

Q3_K_S

4.4 GB

Low

A81

NVFP4Best for your GPU

5.0 GB

Medium

A81

Q4_K_M

5.5 GB