12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can CodeNinja 1.0 OpenChat 7B i1 run on RTX 3070 8GB?
YES — Tight Fit
C53Usable
○Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~7.1 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~73 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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) — 7.1 GB, 73.4 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
73.4 tok/s
TTFT
2636 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RTX 3070 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: 73.4 tok/s decode · 2.6s TTFT (warm) · 184 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 | Tight fit | 73.4 tok/s | 1438 ms | 34K |
| Coding | C | Tight fit | 73.4 tok/s | 2636 ms | 34K |
| Agentic Coding | C | Runs with offload | 73.4 tok/s | 3834 ms | 34K |
| Reasoning | C | Tight fit | 73.4 tok/s | 3115 ms | 34K |
| RAG | C | Runs with offload | 73.4 tok/s | 4793 ms | 34K |
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RTX 3070 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 startUpgrade options
Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well
12 GB VRAM (+4)672 GB/s (+224)
C
Raises estimated decode speed by about 34%.98 tok/s decode
Raises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
12 GB VRAM (+4)504 GB/s (+56)
C
Adds memory headroom for longer context windows and future model growth.90.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP