Would CUDA be a better path than Intel Arc A750 8GB for DevStral 7B?

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 DevStral 7B run on Intel Arc A750 8GB?

Q: What should I upgrade first if DevStral 7B feels slow on Intel Arc A750 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.

YES — With Offload

A77Great

Estimated from fit model

DevStral 7B needs ~7.9 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 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) — 7.9 GB, 55.4 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

55.4 tok/s

TTFT

3493 ms

Safe context

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDevStral 7B on Intel Arc A750 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: 55.4 tok/s decode · 3.5s TTFT (warm) · 139 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	55.4 tok/s	1905 ms	8K
Coding	A	Runs with offload	51.6 tok/s	3755 ms	8K
Agentic Coding	F	Too heavy	26.7 tok/s	10555 ms	8K
Reasoning	A	Runs with offload	55.4 tok/s	4128 ms	8K
RAG	F	Too heavy	26.7 tok/s	13194 ms	8K

Quantization options

How DevStral 7B (7B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A78
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4

Get started

Copy-paste commands to run DevStral 7B on your machine.

Run

ollama run devstral

Your hardware

More models your Intel Arc A750 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	23.1 tok/s
Qwen 3 8B	8B	A	29.9 tok/s

Frequently asked questions

Can Intel Arc A750 8GB run DevStral 7B?

Yes, Intel Arc A750 8GB can run DevStral 7B with a A grade (Runs with offload). Expected decode speed: 51.6 tok/s.

How much VRAM does DevStral 7B need?

DevStral 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DevStral 7B?

The recommended quantization for DevStral 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DevStral 7B run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, DevStral 7B achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3755ms using Q4_K_M quantization.

Can Intel Arc A750 8GB run DevStral 7B for coding?

For coding workloads, DevStral 7B on Intel Arc A750 8GB receives a A grade with 51.6 tok/s and 8K context.

What context window can DevStral 7B use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, DevStral 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if DevStral 7B feels slow on Intel Arc A750 8GB?

See all results for Intel Arc A750 8GB See all hardware for DevStral 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/devstral-7b-on-arc-a750-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: