Would CUDA be a better path than Intel Arc A580 8GB for CodeLlama 13B Instruct?

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 CodeLlama 13B Instruct run on Intel Arc A580 8GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on Intel Arc A580 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeLlama 13B Instruct needs ~21.8 GB but Intel Arc A580 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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) — 21.8 GB, exceeds 8.0 GB available

21.8 GB required8.0 GB available

273% VRAM needed

13.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.7 tok/s

TTFT

40796 ms

Safe context

Memory

21.8 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct 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: 4.7 tok/s decode · 40.8s TTFT (warm) · 12 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.8 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	5.7 tok/s	18481 ms	4K
Coding	F	Too heavy	4.7 tok/s	40796 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59340 ms	4K
Reasoning	F	Too heavy	4.7 tok/s	48214 ms	4K
RAG	F	Too heavy	4.7 tok/s	74175 ms	4K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	5.1 GB	Low	A78
Q3_K_S	3	6.4 GB	Low	F0

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.31.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+24)1792 GB/s (+1280)

Makes the model fit on the accelerator instead of staying completely out of reach.146.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+120)3200 GB/s (+2688)

Makes the model fit on the accelerator instead of staying completely out of reach.182 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+120)3700 GB/s (+3188)

Makes the model fit on the accelerator instead of staying completely out of reach.182 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than Intel Arc A580 8GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 21.8 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, CodeLlama 13B Instruct achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 40796ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Intel Arc A580 8GB receives a F grade with 4.7 tok/s and 4K context.

What context window can CodeLlama 13B Instruct use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, CodeLlama 13B Instruct can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on Intel Arc A580 8GB?

See all results for Intel Arc A580 8GB See all hardware for CodeLlama 13B Instruct

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