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 |
Inference speed
Estimated decode speed (tokens/sec) for StableLM 2 12B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~103 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q5_K_M | 103.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.1 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 53.5 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.1 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 47.8 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.5 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 45.3 | Offloads |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 32.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 32.7 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 25.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 23.8 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 23.4 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.0 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 8.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.8 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.4 | Too big |
Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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 99Upgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 278%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 172%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRaises estimated decode speed by about 82%.
Adds memory headroom for longer context windows and future model growth.
~$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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