Can DeepSeek R1 Distill 7B run on Intel Arc B570 10GB?

YES — Runs Great

A72Great

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.0 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~52 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) — 7.0 GB, 52.2 tok/s, Runs well

7.0 GB required10.0 GB available

70% VRAM used

Fit status

Runs well

Decode

52.2 tok/s

TTFT

3711 ms

Safe context

33K

Memory

7.0 GB / 10.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B 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: 52.2 tok/s decode · 3.7s TTFT (warm) · 130 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	A	Runs well	52.2 tok/s	2024 ms	33K
Coding	A	Runs well	52.2 tok/s	3711 ms	33K
Agentic Coding	A	Runs well	48.1 tok/s	5860 ms	33K
Reasoning	A	Runs well	52.2 tok/s	4385 ms	33K
RAG	A	Runs well	52.2 tok/s	6746 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B68
Q3_K_S	3	3.4 GB	Low	B69
NVFP4	4	3.9 GB	Medium	B70
Q4_K_M	4	4.3 GB	Medium	A70
Q5_K_M	5	5.0 GB	High	B70
Q6_KBest for your GPU	6	5.7 GB	High	B69
Q8_0	8	7.5 GB	Very High	F0
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

Your hardware

More models your Intel Arc B570 10GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	40.2 tok/s
Qwen 3 8B	8B	S	45.2 tok/s
Nemotron Nano 8B	8B	S	45.2 tok/s
InternVL2 8B	8B	A	45.2 tok/s
Ministral 3 8B	8B	A	45.2 tok/s

Frequently asked questions

Can Intel Arc B570 10GB run DeepSeek R1 Distill 7B?

Yes, Intel Arc B570 10GB can run DeepSeek R1 Distill 7B with a A grade (Runs well). Expected decode speed: 52.2 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, DeepSeek R1 Distill 7B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3711ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on Intel Arc B570 10GB receives a A grade with 52.2 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek R1 Distill 7B feels slow on Intel Arc B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB for DeepSeek R1 Distill 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.

See all results for Intel Arc B570 10GB See all hardware for DeepSeek R1 Distill 7B

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