Can CodeNinja 1.0 OpenChat 7B i1 run on Radeon RX 7900M 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 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, 79.6 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

79.6 tok/s

TTFT

2433 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 Radeon RX 7900M 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: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	79.6 tok/s	1327 ms	180K
Coding	C	Runs well	79.6 tok/s	2433 ms	180K
Agentic Coding	C	Runs well	79.6 tok/s	3538 ms	180K
Reasoning	C	Runs well	79.6 tok/s	2875 ms	180K
RAG	C	Runs well	79.6 tok/s	4423 ms	180K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Radeon RX 7900M 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	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
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 Radeon RX 7900M 16GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, Radeon RX 7900M 16GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 79.6 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.6 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 RX 7900M 16GB?

On Radeon RX 7900M 16GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Radeon RX 7900M 16GB receives a C grade with 79.6 tok/s and 180K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 180K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

79.6 tok/s

1327 ms

180K

Coding

Runs well

79.6 tok/s

2433 ms

180K

Agentic Coding

Runs well

79.6 tok/s

3538 ms

180K

Reasoning

Runs well

79.6 tok/s

2875 ms

180K

RAG

Runs well

79.6 tok/s

4423 ms

180K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Radeon RX 7900M 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	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0