32 GB VRAM (+8)1792 GB/s (+1120)
B
Raises estimated decode speed by about 186%.103.1 tok/s decode
Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can StableLM 2 12B run on Quadro RTX 6000 24GB?
YES — With Offload
C51Usable
○Estimated from fit model
StableLM 2 12B needs ~24.1 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q5_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 24.1 GB, 36.0 tok/s, Runs with offload (needs ~0.1 GB host RAM)
24.1 GB required24.0 GB available
100% VRAM needed
0.1 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
36.0 tok/s
TTFT
5373 ms
Safe context
4K
Memory
24.1 GB / 24.0 GB
Memory breakdown
Weights8.6 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsStableLM 2 12B on Quadro RTX 6000 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 36.0 tok/s decode · 5.4s TTFT (warm) · 90 tok/s prefill
What limits this setup
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 50.1 tok/s | 2109 ms | 4K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 36.0 tok/s | 5373 ms | 4K |
| Agentic Coding | F | Too heavy | 14.7 tok/s | 19193 ms | 4K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 36.0 tok/s | 6350 ms | 4K |
| RAG | F | Too heavy | 14.7 tok/s | 23992 ms | 4K |
Quantization options
How StableLM 2 12B (12B params) fits at each quantization level on Quadro RTX 6000 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 |
Get started
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
Hardware that runs StableLM 2 12B well
32 GB VRAM (+8)896 GB/s (+224)
B
Raises estimated decode speed by about 106%.74.2 tok/s decode
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)
C
Raises estimated decode speed by about 38%.49.8 tok/s decode
Raises estimated decode speed by about 38%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP