StableLM 2 12B needs ~24.1 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q5_K_M quantization, expect ~27 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
0.1 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
27.3 tok/s
TTFT
7096 ms
Safe context
4K
Memory
24.1 GB / 24.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 36.8 tok/s | 2866 ms | 4K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 27.3 tok/s | 7096 ms | 4K |
| Agentic Coding | F | Too heavy | 11.5 tok/s | 24421 ms | 4K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 27.3 tok/s | 8386 ms | 4K |
| RAG | F | Too heavy | 11.5 tok/s | 30526 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C45 |
Q3_K_S | 3 | 5.9 GB | Low | C46 |
NVFP4 | 4 | 6.7 GB | Medium | C46 |
Q4_K_M | 4 | 7.3 GB | Medium | C47 |
Q5_K_M | 5 | 8.6 GB | High | C47 |
Q6_K | 6 | 9.8 GB | High | C48 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | C50 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run StableLM 2 12B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "stabilityai/stablelm-2-12b-chat" \
--hf-file "stablelm-2-12b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Opciones de mejora
RTX 5090 32GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 278%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 172%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
RTX 5000 Ada 32GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 82%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$4,000 MSRP
Yes, RTX 4500 Ada 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 27.3 tok/s.
StableLM 2 12B (12B parameters) requires approximately 24.1 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 4500 Ada 24GB, StableLM 2 12B achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7096ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on RTX 4500 Ada 24GB receives a C grade with 27.3 tok/s and 4K context.
On RTX 4500 Ada 24GB, 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-rtx-4500-ada-24gb" 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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