Can DeepSeek R1 Distill 7B run on Intel Arc A730M 12GB?

YES — Runs Great

B69Good

○Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.2 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 7.2 GB, 41.9 tok/s, Runs well

7.2 GB required12.0 GB available

60% VRAM used

Fit status

Runs well

Decode

41.9 tok/s

TTFT

4625 ms

Safe context

33K

Memory

7.2 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on Intel Arc A730M 12GB
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: 41.9 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	41.9 tok/s	2523 ms	33K
Coding	B	Runs well	41.9 tok/s	4625 ms	33K
Agentic Coding	A	Runs well	41.9 tok/s	6727 ms	33K
Reasoning	B	Runs well	41.9 tok/s	5466 ms	33K
RAG	A	Runs well	41.9 tok/s	8409 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B67
Q4_K_M	4	4.3 GB	Medium	B68
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	B69
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.

Run

ollama run deepseek-r1:7b

Frequently asked questions

See all results for Intel Arc A730M 12GB See all hardware for DeepSeek R1 Distill 7B

URL: https://willitrunai.com/can-run/deepseek-r1-distill-qwen-7b-on-arc-a730m-12gb

⇱ DeepSeek R1 Distill 7B on Intel Arc A730M 12GB? YES