MacBook Pro M4 Max 48GBOpção econômica
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.8 tok/s decodificação
~$2,499 MSRP
Can CodeNinja 1.0 OpenChat 7B i1 run on RTX 5000 Ada 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~9.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.5 GB, 98.0 tok/s, Runs well
9.5 GB required32.0 GB available
30% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
455K
Memory
9.5 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RTX 5000 Ada 32GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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 | 98.0 tok/s | 1078 ms | 455K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 455K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 455K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 455K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 455K |
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C42 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C47 |
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 startOpções de upgrade
Hardware que roda bem CodeNinja 1.0 OpenChat 7B i1
Frequently asked questions
Can RTX 5000 Ada 32GB run CodeNinja 1.0 OpenChat 7B i1?
Yes, RTX 5000 Ada 32GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?
CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 9.5 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 5000 Ada 32GB?
On RTX 5000 Ada 32GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can RTX 5000 Ada 32GB run CodeNinja 1.0 OpenChat 7B i1 for coding?
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RTX 5000 Ada 32GB receives a C grade with 98.0 tok/s and 455K context.
What context window can CodeNinja 1.0 OpenChat 7B i1 use on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 455K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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