Would CUDA be a better path than Intel Arc B570 10GB for Qwen3-Coder-Next?

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 Qwen3-Coder-Next run on Intel Arc B570 10GB?

Q: What should I upgrade first if Qwen3-Coder-Next feels slow on Intel Arc B570 10GB?

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

Qwen3-Coder-Next needs ~52.2 GB but Intel Arc B570 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: 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) — 52.2 GB, exceeds 10.0 GB available

52.2 GB required10.0 GB available

522% VRAM needed

42.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

93141 ms

Safe context

Memory

52.2 GB / 10.0 GB

Offload

80%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder-Next on Intel Arc B570 10GB
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: 2.1 tok/s decode · 93.1s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 52.2 GB, but this setup only exposes 10.0 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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.1 tok/s	93141 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	135478 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	110076 ms	4K
RAG	F	Too heavy	2.1 tok/s	169347 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	F0
Q3_K_S	3	39.2 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen3-Coder-Next well

Intel Data Center GPU Max 1550 128GBBudget pick

128 GB VRAM (+118)3200 GB/s (+2820)

Makes the model fit on the accelerator instead of staying completely out of reach.136.1 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.

~$15,000 MSRP

Gaudi 3 128GBBest value

128 GB VRAM (+118)3700 GB/s (+3320)

Makes the model fit on the accelerator instead of staying completely out of reach.174.9 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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Qwen3-Coder-Next?

No, Qwen3-Coder-Next requires more memory than Intel Arc B570 10GB provides.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 52.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Qwen3-Coder-Next achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 93141ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Intel Arc B570 10GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder-Next feels slow on Intel Arc B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for Qwen3-Coder-Next

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