Qwen 2.5 14B needs ~14.3 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
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
Fit status
Tight fit
Decode
54.6 tok/s
TTFT
3545 ms
Safe context
25K
Memory
14.3 GB / 16.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 54.6 tok/s | 1933 ms | 25K |
| Coding | A | Tight fit | 54.6 tok/s | 3545 ms | 25K |
| Agentic Coding | A | Runs with offload (needs ~0.6 GB host RAM) | 34.0 tok/s | 8285 ms | 25K |
| Reasoning | A | Tight fit | 54.6 tok/s | 4189 ms | 25K |
| RAG | A | Runs with offload (needs ~0.6 GB host RAM) | 34.0 tok/s | 10356 ms | 25K |
How Qwen 2.5 14B (14B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A80 |
Q3_K_S | 3 | 6.9 GB | Low | A82 |
NVFP4 | 4 | 7.8 GB | Medium | A82 |
Q4_K_M | 4 | 8.5 GB | Medium | A82 |
Q5_K_M | 5 | 10.1 GB | High | A82 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | A82 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Phi-4-reasoning-plus 14B | 14.7B | S | 51.8 tok/s | |
 GPT-OSS 20B | 21B | A | 46.4 tok/s | |
 Codestral 2 25.08 | 22B | A | 18 tok/s | |
 CogVLM2 19B | 19B | A | 26.1 tok/s |
Yes, Tesla P100 16GB can run Qwen 2.5 14B with a A grade (Tight fit). Expected decode speed: 54.6 tok/s.
Qwen 2.5 14B (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 14B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P100 16GB, Qwen 2.5 14B achieves approximately 54.6 tokens per second decode speed with a time-to-first-token of 3545ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 14B on Tesla P100 16GB receives a A grade with 54.6 tok/s and 25K context.
On Tesla P100 16GB, Qwen 2.5 14B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-14b-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>
Preview: