Can CodeNinja 1.0 OpenChat 7B i1 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C47Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~9.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.2 GB, 88.4 tok/s, Runs well

9.2 GB required32.0 GB available

29% VRAM used

Fit status

Runs well

Decode

88.4 tok/s

TTFT

2189 ms

Safe context

461K

Memory

9.2 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on Radeon AI PRO R9700 32GB
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: 88.4 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	88.4 tok/s	1194 ms	461K
Coding	C	Runs well	88.4 tok/s	2189 ms	461K
Agentic Coding	C	Runs well	88.4 tok/s	3184 ms	461K
Reasoning	C	Runs well	88.4 tok/s	2587 ms	461K
RAG	C	Runs well	88.4 tok/s	3981 ms	461K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C43
NVFP4	4

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

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.87.8 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, Radeon AI PRO R9700 32GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 88.4 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 9.2 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Radeon AI PRO R9700 32GB receives a C grade with 88.4 tok/s and 461K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: