Would CUDA be a better path than Intel Arc A750 8GB for DeepSeek V4 Flash?

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 DeepSeek V4 Flash run on Intel Arc A750 8GB?

Q: What should I upgrade first if DeepSeek V4 Flash feels slow on Intel Arc A750 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

DeepSeek V4 Flash needs ~161.0 GB but Intel Arc A750 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

NVFP4 (Medium quality) — 161.0 GB, exceeds 8.0 GB available

161.0 GB required8.0 GB available

2013% VRAM needed

153.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

161.0 GB / 8.0 GB

Offload

100%

Memory breakdown

Weights158.0 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Flash 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: 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 161.0 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	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

Quantization options

How DeepSeek V4 Flash (284B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	110.8 GB	Low	F0
Q3_K_S	3	139.2 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs DeepSeek V4 Flash well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+248)819 GB/s (+307)

Makes the model fit on the accelerator instead of staying completely out of reach.17.8 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.

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+280)8000 GB/s (+7488)

Makes the model fit on the accelerator instead of staying completely out of reach.125.8 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.

~$8,000 MSRP

Frequently asked questions

Can Intel Arc A750 8GB run DeepSeek V4 Flash?

No, DeepSeek V4 Flash requires more memory than Intel Arc A750 8GB provides.

How much VRAM does DeepSeek V4 Flash need?

DeepSeek V4 Flash (284B parameters) requires approximately 161.0 GB of memory with NVFP4 quantization.

What is the best quantization for DeepSeek V4 Flash?

The recommended quantization for DeepSeek V4 Flash is NVFP4, which balances quality and memory efficiency.

What speed will DeepSeek V4 Flash run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, DeepSeek V4 Flash achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using NVFP4 quantization.

Can Intel Arc A750 8GB run DeepSeek V4 Flash for coding?

For coding workloads, DeepSeek V4 Flash on Intel Arc A750 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek V4 Flash use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, DeepSeek V4 Flash can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V4 Flash feels slow on Intel Arc A750 8GB?

See all results for Intel Arc A750 8GB See all hardware for DeepSeek V4 Flash

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