Can CodeNinja 1.0 OpenChat 7B i1 run on RX 6700 XT 12GB?

YES — Runs Great

C52Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.2 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~47 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, 46.8 tok/s, Runs well

7.2 GB required12.0 GB available

60% VRAM used

Fit status

Runs well

Decode

46.8 tok/s

TTFT

4140 ms

Safe context

110K

Memory

7.2 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RX 6700 XT 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: 46.8 tok/s decode · 4.1s TTFT (warm) · 117 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	46.8 tok/s	2258 ms	110K
Coding	C	Runs well	46.8 tok/s	4140 ms	110K
Agentic Coding	C	Runs well	46.8 tok/s	6022 ms	110K
Reasoning	C	Runs well	46.8 tok/s	4893 ms	110K
RAG	C	Runs well	46.8 tok/s	7527 ms	110K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C48
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4	3.9 GB	Medium	C50
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C51
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
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

Frequently asked questions

Can RX 6700 XT 12GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, RX 6700 XT 12GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 46.8 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.2 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 RX 6700 XT 12GB?

On RX 6700 XT 12GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 46.8 tokens per second decode speed with a time-to-first-token of 4140ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RX 6700 XT 12GB receives a C grade with 46.8 tok/s and 110K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 110K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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