StableLM 2 12B needs ~22.3 GB but GTX 1660 Super 6GB only has 6.0 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
16.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
64683 ms
Safe context
4K
Memory
22.3 GB / 6.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.3 GB, but this setup only exposes 6.0 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 | F | Too heavy | 3.0 tok/s | 35282 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 64683 ms | 4K |
| Agentic Coding | F | Too heavy | 3.0 tok/s | 94084 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76443 ms | 4K |
| RAG | F | Too heavy | 3.0 tok/s | 117605 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | F0 |
Q3_K_S | 3 | 5.9 GB | Low | F0 |
NVFP4 | 4 | 6.7 GB | Medium | F0 |
Q4_K_M | 4 | 7.3 GB | Medium | F0 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Upgrade-Optionen
RTX 4000 Ada 20GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 427%.
ca. $1,250 MSRP
RTX 3090 24GBGünstige WahlMakes 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.
ca. $1,499 MSRP
RTX 4090 24GBBestes Preis-Leistungs-VerhältnisMakes 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.
ca. $1,599 MSRP
No, StableLM 2 12B requires more memory than GTX 1660 Super 6GB provides.
StableLM 2 12B (12B parameters) requires approximately 22.3 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 GTX 1660 Super 6GB, StableLM 2 12B achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 64683ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on GTX 1660 Super 6GB receives a F grade with 3.0 tok/s and 4K context.
On GTX 1660 Super 6GB, 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-gtx-1660-super-6gb" 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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