Mac Studio M2 Ultra 128GBOpção econômica
128 GB Unified (+48)
C
This setup is broadly balanced for this model.102.2 tok/s decodificação
~$3,999 MSRP
Can Dolphin 2.9 8B run on NVIDIA H100 80GB?
YES — Runs Great
C47Usable
Estimated from fit model
Dolphin 2.9 8B needs ~16.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~112 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) — 16.0 GB, 112.0 tok/s, Runs well
16.0 GB required80.0 GB available
20% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
33K
Memory
16.0 GB / 80.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsDolphin 2.9 8B 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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 | 112.0 tok/s | 943 ms | 33K |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 33K |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 33K |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 33K |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 33K |
Quantization options
How Dolphin 2.9 8B (8B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C40 |
Q3_K_S | 3 | 3.9 GB | Low | C40 |
NVFP4 | 4 | 4.5 GB | Medium | C40 |
Q4_K_M | 4 | 4.9 GB | Medium | C40 |
Q5_K_M | 5 | 5.8 GB | High | C40 |
Q6_K | 6 | 6.6 GB | High | C40 |
Q8_0 | 8 | 8.6 GB | Very High | C41 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C42 |
Get started
Copy-paste commands to run Dolphin 2.9 8B on your machine.
Run
ollama run dolphin-llama3Opções de upgrade
Hardware que roda bem Dolphin 2.9 8B
Frequently asked questions
Can NVIDIA H100 80GB run Dolphin 2.9 8B?
Yes, NVIDIA H100 80GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does Dolphin 2.9 8B need?
Dolphin 2.9 8B (8B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Dolphin 2.9 8B?
The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Dolphin 2.9 8B run at on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Dolphin 2.9 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can NVIDIA H100 80GB run Dolphin 2.9 8B for coding?
For coding workloads, Dolphin 2.9 8B on NVIDIA H100 80GB receives a C grade with 112.0 tok/s and 33K context.
What context window can Dolphin 2.9 8B use on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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/dolphin-2.9-8b-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: