Nous Dolphin 13B needs ~24.4 GB but Tesla P100 16GB only has 16.0 GB. Try a smaller quantization or lighter model.
Operating mode
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
8.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.6 tok/s
TTFT
14204 ms
Safe context
5K
Memory
24.4 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 24.4 GB, but this setup only exposes 16.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~1.2 GB host RAM) | 25.7 tok/s | 4113 ms | 5K |
| Coding | F | Too heavy | 13.6 tok/s | 14204 ms | 5K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39889 ms | 5K |
| Reasoning | F | Too heavy | 13.6 tok/s | 16787 ms | 5K |
| RAG | F | Too heavy | 7.1 tok/s | 49861 ms | 5K |
How Nous Dolphin 13B (13B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B70 |
Q3_K_S | 3 | 6.4 GB | Low | A71 |
NVFP4 | 4 | 7.3 GB | Medium | A72 |
Q4_K_M | 4 | 7.9 GB | Medium | A72 |
Q5_K_M | 5 | 9.4 GB | High | A72 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A72 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
アップグレードオプション
RTX 3090 24GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,499 MSRP
RTX 4090 24GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,599 MSRP
No, Nous Dolphin 13B requires more memory than Tesla P100 16GB provides.
Nous Dolphin 13B (13B parameters) requires approximately 24.4 GB of memory with Q5_K_M quantization.
The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.
On Tesla P100 16GB, Nous Dolphin 13B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14204ms using Q5_K_M quantization.
For coding workloads, Nous Dolphin 13B on Tesla P100 16GB receives a F grade with 13.6 tok/s and 5K context.
On Tesla P100 16GB, Nous Dolphin 13B can safely use up to 5K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-dolphin-13b-on-tesla-p100-16gb" 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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