Can CodeNinja 1.0 OpenChat 7B i1 run on RX 5700 XT 8GB?

YES — Tight Fit

C52Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~6.8 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 6.8 GB, 54.6 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

54.6 tok/s

TTFT

3549 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RX 5700 XT 8GB
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: 54.6 tok/s decode · 3.5s TTFT (warm) · 136 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	54.6 tok/s	1936 ms	40K
Coding	C	Tight fit	54.6 tok/s	3549 ms	40K
Agentic Coding	C	Runs with offload	54.6 tok/s	5162 ms	40K
Reasoning	C	Tight fit	54.6 tok/s	4194 ms	40K
RAG	C	Runs with offload	54.6 tok/s	6452 ms	40K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	F0
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

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

RX 7700 XT 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.60.7 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RX 6700 XT 12GBBest value

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.46.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

RX 6750 XT 12GBAMD upgrade

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.53.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

Frequently asked questions

Can RX 5700 XT 8GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, RX 5700 XT 8GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Tight fit). Expected decode speed: 54.6 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 6.8 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 5700 XT 8GB?

On RX 5700 XT 8GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 54.6 tokens per second decode speed with a time-to-first-token of 3549ms using Q4_K_M quantization.

Can RX 5700 XT 8GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RX 5700 XT 8GB receives a C grade with 54.6 tok/s and 40K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 5700 XT 8GB 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-5700-xt-8gb" 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

54.6 tok/s

1936 ms

40K

Coding

Tight fit

54.6 tok/s

3549 ms

40K

Agentic Coding

Runs with offload

54.6 tok/s

5162 ms

40K

Reasoning

Tight fit

54.6 tok/s

4194 ms

40K

RAG

Runs with offload

54.6 tok/s

6452 ms

40K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0