Mac Studio M2 Ultra 128GBBudget pick
128 GB Unified (+48)
C
This setup is broadly balanced for this model.76.1 tok/s decode
~$3,999 MSRP
Can HelpingAI2.5 10B i1 run on NVIDIA H100 80GB?
YES — Runs Great
C46Usable
○Estimated from fit model
HelpingAI2.5 10B i1 needs ~16.5 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~140 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 16.5 GB, 140.0 tok/s, Runs well
16.5 GB required80.0 GB available
21% VRAM used
Fit status
Runs well
Decode
140.0 tok/s
TTFT
1383 ms
Safe context
883K
Memory
16.5 GB / 80.0 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA H100 80GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 140.0 tok/s decode · 1.4s TTFT (warm) · 350 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 | 140.0 tok/s | 754 ms | 883K |
| Coding | C | Runs well | 140.0 tok/s | 1383 ms | 883K |
| Agentic Coding | C | Runs well | 140.0 tok/s | 2011 ms | 883K |
| Reasoning | C | Runs well | 140.0 tok/s | 1634 ms | 883K |
| RAG | C | Runs well | 140.0 tok/s | 2514 ms | 883K |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | D39 |
Q3_K_S | 3 | 4.9 GB | Low | D39 |
NVFP4 | 4 | 5.6 GB | Medium | D39 |
Q4_K_M | 4 | 6.1 GB | Medium | D39 |
Q5_K_M | 5 | 7.2 GB | High | D39 |
Q6_K | 6 | 8.2 GB | High | D39 |
Q8_0 | 8 | 10.7 GB | Very High | D40 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C41 |
Get started
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startUpgrade options