RX 7900 XT 20GBコスパ最良
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 99%.98 tok/sデコード
Raises estimated decode speed by about 99%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can CodeNinja 1.0 OpenChat 7B i1 run on RTX 4060 Ti 16GB?
YES — Runs Great
C50Usable
Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~7.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.9 GB, 49.2 tok/s, Runs well
7.9 GB required16.0 GB available
49% VRAM used
Fit status
Runs well
Decode
49.2 tok/s
TTFT
3932 ms
Safe context
174K
Memory
7.9 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RTX 4060 Ti 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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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 | 49.2 tok/s | 2145 ms | 174K |
| Coding | C | Runs well | 49.2 tok/s | 3932 ms | 174K |
| Agentic Coding | C | Runs well | 49.2 tok/s | 5719 ms | 174K |
| Reasoning | C | Runs well | 49.2 tok/s | 4647 ms | 174K |
| RAG | C | Runs well | 49.2 tok/s | 7149 ms | 174K |
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RTX 4060 Ti 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アップグレードオプション
CodeNinja 1.0 OpenChat 7B i1を快適に動かすハードウェア
Frequently asked questions
Can RTX 4060 Ti 16GB run CodeNinja 1.0 OpenChat 7B i1?
Yes, RTX 4060 Ti 16GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 49.2 tok/s.
How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?
CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.9 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 RTX 4060 Ti 16GB?
On RTX 4060 Ti 16GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.
Can RTX 4060 Ti 16GB run CodeNinja 1.0 OpenChat 7B i1 for coding?
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RTX 4060 Ti 16GB receives a C grade with 49.2 tok/s and 174K context.
What context window can CodeNinja 1.0 OpenChat 7B i1 use on RTX 4060 Ti 16GB?
On RTX 4060 Ti 16GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 174K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-rtx-4060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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