StableLM 2 12B needs ~23.3 GB but RTX 5000 Ada Laptop 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
16.1 tok/s
TTFT
11996 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.
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 | C | Runs with offload (needs ~0.6 GB host RAM) | 30.5 tok/s | 3458 ms | 4K |
| Coding | F | Too heavy | 16.1 tok/s | 11996 ms | 4K |
| Agentic Coding | F | Too heavy | 7.2 tok/s | 39381 ms | 4K |
| Reasoning | F | Too heavy | 16.1 tok/s | 14177 ms | 4K |
| RAG | F | Too heavy | 7.2 tok/s | 49226 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on RTX 5000 Ada Laptop 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 |
Upgrade options
RTX 3090 24GBBudget pickMakes 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 24GBBest valueMakes 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 5090 32GBNVIDIA upgradeMakes 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.
~$1,999 MSRP
No, StableLM 2 12B requires more memory than RTX 5000 Ada Laptop 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 RTX 5000 Ada Laptop 16GB, StableLM 2 12B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 11996ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on RTX 5000 Ada Laptop 16GB receives a F grade with 16.1 tok/s and 4K context.
On RTX 5000 Ada Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-rtx-5000-ada-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.
