Vicuna 13B needs ~22.9 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
6.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
18.0 tok/s
TTFT
10748 ms
Safe context
4K
Memory
22.9 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.9 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 | A | Runs with offload (needs ~0.4 GB host RAM) | 35.5 tok/s | 2972 ms | 4K |
| Coding | F | Too heavy | 18.0 tok/s | 10748 ms | 4K |
| Agentic Coding | F | Too heavy | 8.2 tok/s | 34471 ms | 4K |
| Reasoning | F | Too heavy | 18.0 tok/s | 12702 ms | 4K |
| RAG | F | Too heavy | 8.2 tok/s | 43089 ms | 4K |
How Vicuna 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 |
升级选项
RTX 4000 Ada 20GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GB经济之选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 PRO 4000 Blackwell 24GBNVIDIA 升级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, Vicuna 13B requires more memory than Tesla P100 16GB provides.
Vicuna 13B (13B parameters) requires approximately 22.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P100 16GB, Vicuna 13B achieves approximately 18.0 tokens per second decode speed with a time-to-first-token of 10748ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on Tesla P100 16GB receives a F grade with 18.0 tok/s and 4K context.
On Tesla P100 16GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/vicuna-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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