Can CodeNinja 1.0 OpenChat 7B i1 run on Intel Arc B570 10GB?

YES — Runs Great

C54Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.0 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~48 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, 48.1 tok/s, Runs well

7.0 GB required10.0 GB available

70% VRAM used

Fit status

Runs well

Decode

48.1 tok/s

TTFT

4029 ms

Safe context

75K

Memory

7.0 GB / 10.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 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: 48.1 tok/s decode · 4.0s TTFT (warm) · 120 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	C	Runs well	48.1 tok/s	2197 ms	75K
Coding	C	Runs well	48.1 tok/s	4029 ms	75K
Agentic Coding	C	Runs well	48.1 tok/s	5860 ms	75K
Reasoning	C	Runs well	48.1 tok/s	4761 ms	75K
RAG	C	Runs well	48.1 tok/s	7325 ms	75K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (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	C50
Q3_K_S	3	3.4 GB	Low	C51
NVFP4	4	3.9 GB	Medium	C52
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_M	5	5.0 GB	High	C52
Q6_KBest for your GPU	6	5.7 GB	High	C52
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

アップグレードオプション

CodeNinja 1.0 OpenChat 7B i1を快適に動かすハードウェア

RTX 2080 Ti 11GBコスパ重視

11 GB VRAM (+1)616 GB/s (+236)

Raises estimated decode speed by about 95%.93.8 tok/sデコード

Raises estimated decode speed by about 95%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$999 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, Intel Arc B570 10GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 48.1 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4029ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Intel Arc B570 10GB receives a C grade with 48.1 tok/s and 75K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 75K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if CodeNinja 1.0 OpenChat 7B i1 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 CodeNinja 1.0 OpenChat 7B i1?

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 CodeNinja 1.0 OpenChat 7B i1

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