MacBook Pro M4 Max 96GBOpção econômica
96 GB Unified (+32)
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 NVIDIA A16 64GB?
YES — Runs Great
C46Usable
Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~12.7 GB VRAM. NVIDIA A16 64GB has 64.0 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) — 12.7 GB, 98.0 tok/s, Runs well
12.7 GB required64.0 GB available
20% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
1.0M
Memory
12.7 GB / 64.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on NVIDIA A16 64GB
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 | 1.0M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 1.0M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 1.0M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 1.0M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 1.0M |
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D40 |
Q3_K_S | 3 | 3.4 GB | Low | D40 |
NVFP4 | 4 | 3.9 GB | Medium | D40 |
Q4_K_M | 4 | 4.3 GB | Medium | D40 |
Q5_K_M | 5 | 5.0 GB | High | C40 |
Q6_K | 6 | 5.7 GB | High | C40 |
Q8_0 | 8 | 7.5 GB | Very High | C40 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C42 |
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 NVIDIA A16 64GB run CodeNinja 1.0 OpenChat 7B i1?
Yes, NVIDIA A16 64GB 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 12.7 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, 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 NVIDIA A16 64GB run CodeNinja 1.0 OpenChat 7B i1 for coding?
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on NVIDIA A16 64GB receives a C grade with 98.0 tok/s and 1.0M context.
What context window can CodeNinja 1.0 OpenChat 7B i1 use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 1.0M 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-a16-64gb" 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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