40 GB VRAM (+8)1555 GB/s (+655)
B
Raises estimated decode speed by about 95%.115.7 tok/s decode
Raises estimated decode speed by about 95%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can StableLM 2 12B run on NVIDIA V100 32GB?
YES — Runs Great
B55Good
○Estimated from fit model
StableLM 2 12B needs ~24.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q5_K_M quantization, expect ~59 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 24.9 GB, 59.3 tok/s, Runs well
24.9 GB required32.0 GB available
78% VRAM used
Fit status
Runs well
Decode
59.3 tok/s
TTFT
3267 ms
Safe context
4K
Memory
24.9 GB / 32.0 GB
Memory breakdown
Weights8.6 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsStableLM 2 12B on NVIDIA V100 32GB
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: 59.3 tok/s decode · 3.3s TTFT (warm) · 148 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 59.3 tok/s | 1782 ms | 4K |
| Coding | B | Runs well | 59.3 tok/s | 3267 ms | 4K |
| Agentic Coding | C | Very compromised | 39.5 tok/s | 7129 ms | 4K |
| Reasoning | B | Runs well | 59.3 tok/s | 3861 ms | 4K |
| RAG | C | Very compromised | 39.5 tok/s | 8911 ms | 4K |
Quantization options
How StableLM 2 12B (12B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C43 |
Q3_K_S | 3 | 5.9 GB | Low | C44 |
NVFP4 | 4 |
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