StableLM 2 12B needs ~23.3 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
7.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.8 tok/s
TTFT
13051 ms
Safe context
4K
Memory
23.3 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.3 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 | D | Runs with offload (needs ~0.6 GB host RAM) | 28.9 tok/s | 3659 ms | 4K |
| Coding | F | Too heavy | 14.8 tok/s | 13051 ms | 4K |
| Agentic Coding | F | Too heavy | 7.0 tok/s | 40258 ms | 4K |
| Reasoning | F | Too heavy | 14.8 tok/s | 15424 ms | 4K |
| RAG | F | Too heavy | 7.0 tok/s | 50323 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C48 |
Q3_K_S | 3 | 5.9 GB | Low | C49 |
NVFP4 | 4 | 6.7 GB | Medium | C50 |
Q4_K_M | 4 | 7.3 GB | Medium | C51 |
Q5_K_M | 5 | 8.6 GB | High | C51 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | C51 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Opções de upgrade
RTX 4000 Ada 20GBUpgrade NVIDIAMakes 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 24GBOpção econômicaMakes 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 24GBMelhor custo-benefícioMakes 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, StableLM 2 12B requires more memory than Tesla P100 16GB provides.
StableLM 2 12B (12B parameters) requires approximately 23.3 GB of memory with Q5_K_M quantization.
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
On Tesla P100 16GB, StableLM 2 12B achieves approximately 14.8 tokens per second decode speed with a time-to-first-token of 13051ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on Tesla P100 16GB receives a F grade with 14.8 tok/s and 4K context.
On Tesla P100 16GB, StableLM 2 12B 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/stablelm-2-12b-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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