Would CUDA be a better path than Intel Arc A370M 4GB for Laguna XS 2.1?

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 Laguna XS 2.1 run on Intel Arc A370M 4GB?

Q: What should I upgrade first if Laguna XS 2.1 feels slow on Intel Arc A370M 4GB?

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

Laguna XS 2.1 needs ~24.1 GB but Intel Arc A370M 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very 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) — 22.3 GB, exceeds 4.0 GB available

22.3 GB required4.0 GB available

558% VRAM needed

18.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

22.3 GB / 4.0 GB

Offload

80%

Memory breakdown

Weights20.4 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLaguna XS 2.1 on Intel Arc A370M 4GB
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: 2.0 tok/s decode · 96.8s 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 24.1 GB, but this setup only exposes 4.0 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	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Laguna XS 2.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Laguna XS 2.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	144.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	85.8	Offloads

Quantization options

How Laguna XS 2.1 (33.400001525878906B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	4.8 GB	Very Low	F0
Q2_0_G128	1.71	8.9 GB	Low	F0
Q2_K	2

Upgrade options

Hardware that runs Laguna XS 2.1 well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+20)456 GB/s (+344)

Makes the model fit on the accelerator instead of staying completely out of reach.26.3 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 (+28)1792 GB/s (+1680)

Makes the model fit on the accelerator instead of staying completely out of reach.144.2 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 (+124)3200 GB/s (+3088)

Makes the model fit on the accelerator instead of staying completely out of reach.288.3 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 (+124)3700 GB/s (+3588)

Makes the model fit on the accelerator instead of staying completely out of reach.370.3 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 A370M 4GB run Laguna XS 2.1?

No, Laguna XS 2.1 requires more memory than Intel Arc A370M 4GB provides.

How much VRAM does Laguna XS 2.1 need?

Laguna XS 2.1 (33.400001525878906B parameters) requires approximately 24.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Laguna XS 2.1?

The recommended quantization for Laguna XS 2.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Laguna XS 2.1 run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Laguna XS 2.1 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run Laguna XS 2.1 for coding?

For coding workloads, Laguna XS 2.1 on Intel Arc A370M 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Laguna XS 2.1 use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Laguna XS 2.1 can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Laguna XS 2.1 feels slow on Intel Arc A370M 4GB?

See all results for Intel Arc A370M 4GB See all hardware for Laguna XS 2.1

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