RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+856)
B
Raises estimated decode speed by about 128%.103.1 tok/s 解码
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can StableLM 2 12B run on RTX 3090 24GB?
YES — With Offload
C51Usable
Estimated from fit model
StableLM 2 12B needs ~24.1 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q5_K_M quantization, expect ~45 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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) — 24.1 GB, 45.3 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
45.3 tok/s
TTFT
4276 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 RTX 3090 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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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.
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 | B | Runs well | 61.1 tok/s | 1727 ms | 4K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 45.3 tok/s | 4276 ms | 4K |
| Agentic Coding | F | Too heavy | 19.1 tok/s | 14717 ms | 4K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 45.3 tok/s | 5054 ms | 4K |
| RAG | F | Too heavy | 19.1 tok/s | 18396 ms | 4K |
Inference speed
StableLM 2 12B inference speed — tokens per second by GPU & Mac
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.
Quantization options
How StableLM 2 12B (12B params) fits at each quantization level on RTX 3090 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 99升级选项
能流畅运行 StableLM 2 12B 的硬件
RTX 5090 32GB经济之选RTX PRO 4500 Blackwell 32GB最佳性价比32 GB VRAM (+8)
B
Raises estimated decode speed by about 64%.74.2 tok/s 解码
Raises estimated decode speed by about 64%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.49.8 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Frequently asked questions
Can RTX 3090 24GB run StableLM 2 12B?
Yes, RTX 3090 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 45.3 tok/s.
How much VRAM does StableLM 2 12B need?
StableLM 2 12B (12B parameters) requires approximately 24.1 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 3090 24GB?
On RTX 3090 24GB, StableLM 2 12B achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4276ms using Q5_K_M quantization.
Can RTX 3090 24GB run StableLM 2 12B for coding?
For coding workloads, StableLM 2 12B on RTX 3090 24GB receives a C grade with 45.3 tok/s and 4K context.
What context window can StableLM 2 12B use on RTX 3090 24GB?
On RTX 3090 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.
What should I upgrade first if StableLM 2 12B feels slow on RTX 3090 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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