NVIDIA A100 40GBコスパ重視40 GB VRAM (+8)1555 GB/s (+979)
B
Raises estimated decode speed by about 132%.115.7 tok/sデコード
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
〜$10,000 MSRP
Can StableLM 2 12B run on RTX 5000 Ada 32GB?
YES — Runs Great
C55Usable
Estimated from fit model
StableLM 2 12B needs ~24.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q5_K_M quantization, expect ~50 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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, 49.8 tok/s, Runs well
24.9 GB required32.0 GB available
78% VRAM used
Fit status
Runs well
Decode
49.8 tok/s
TTFT
3891 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 RTX 5000 Ada 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: 49.8 tok/s decode · 3.9s TTFT (warm) · 124 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 | 49.8 tok/s | 2122 ms | 4K |
| Coding | C | Runs well | 49.8 tok/s | 3891 ms | 4K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 27.3 tok/s | 10334 ms | 4K |
| Reasoning | C | Runs well | 49.8 tok/s | 4599 ms | 4K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 27.3 tok/s | 12917 ms | 4K |
Quantization options
How StableLM 2 12B (12B params) fits at each quantization level on RTX 5000 Ada 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 | 6.7 GB | Medium | C44 |
Q4_K_M | 4 | 7.3 GB | Medium | C44 |
Q5_K_M | 5 | 8.6 GB | High | C45 |
Q6_K | 6 | 9.8 GB | High | C45 |
Q8_0 | 8 | 12.8 GB | Very High | C47 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C48 |
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 99アップグレードオプション
StableLM 2 12Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 5000 Ada 32GB run StableLM 2 12B?
Yes, RTX 5000 Ada 32GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 49.8 tok/s.
How much VRAM does StableLM 2 12B need?
StableLM 2 12B (12B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.
What is the best quantization for StableLM 2 12B?
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
What speed will StableLM 2 12B run at on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, StableLM 2 12B achieves approximately 49.8 tokens per second decode speed with a time-to-first-token of 3891ms using Q5_K_M quantization.
Can RTX 5000 Ada 32GB run StableLM 2 12B for coding?
For coding workloads, StableLM 2 12B on RTX 5000 Ada 32GB receives a C grade with 49.8 tok/s and 4K context.
What context window can StableLM 2 12B use on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, 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.
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