Can CodeNinja 1.0 OpenChat 7B i1 run on RX 9070 16GB?

YES — Runs Great

C52Usable

○Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.6 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~93 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) — 7.6 GB, 92.9 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

92.9 tok/s

TTFT

2083 ms

Safe context

180K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RX 9070 16GB
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: 92.9 tok/s decode · 2.1s TTFT (warm) · 232 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	92.9 tok/s	1136 ms	180K
Coding	C	Runs well	92.9 tok/s	2083 ms	180K
Agentic Coding	C	Runs well	92.9 tok/s	3030 ms	180K
Reasoning	C	Runs well	92.9 tok/s	2462 ms	180K
RAG	C	Runs well	92.9 tok/s	3788 ms	180K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
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

Frequently asked questions

See all results for RX 9070 16GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-rx-9070-16gb

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