Mac Studio M2 Ultra 128GBコスパ重視
128 GB Unified (+48)
C
This setup is broadly balanced for this model.98 tok/sデコード
〜$3,999 MSRP
Can CodeNinja 1.0 OpenChat 7B i1 run on NVIDIA H100 80GB?
YES — Runs Great
C45Usable
Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~14.3 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 14.3 GB, 98.0 tok/s, Runs well
14.3 GB required80.0 GB available
18% VRAM used
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
1.3M
Memory
14.3 GB / 80.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on NVIDIA H100 80GB
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 | 1.3M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 1.3M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 1.3M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 1.3M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 1.3M |
Inference speed
CodeNinja 1.0 OpenChat 7B i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for CodeNinja 1.0 OpenChat 7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D39 |
Q3_K_S | 3 | 3.4 GB | Low | D39 |
NVFP4 | 4 | 3.9 GB | Medium | D39 |
Q4_K_M | 4 | 4.3 GB | Medium | D39 |
Q5_K_M | 5 | 5.0 GB | High | D39 |
Q6_K | 6 | 5.7 GB | High | D39 |
Q8_0 | 8 | 7.5 GB | Very High | D39 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C40 |
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 NVIDIA H100 80GB run CodeNinja 1.0 OpenChat 7B i1?
Yes, NVIDIA H100 80GB 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 14.3 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 H100 80GB?
On NVIDIA H100 80GB, 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 H100 80GB run CodeNinja 1.0 OpenChat 7B i1 for coding?
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on NVIDIA H100 80GB receives a C grade with 98.0 tok/s and 1.3M context.
What context window can CodeNinja 1.0 OpenChat 7B i1 use on NVIDIA H100 80GB?
On NVIDIA H100 80GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 1.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: