Can StableLM 2 12B run on RTX 4000 Ada 20GB?

BARELY — Tight on Memory

D32Poor

Estimated from fit model

StableLM 2 12B needs ~23.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q5_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 23.7 GB, 15.8 tok/s, Very compromised (needs ~1.4 GB host RAM)

23.7 GB required20.0 GB available

119% VRAM needed

3.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.4 GB host RAM)

Decode

15.8 tok/s

TTFT

12221 ms

Safe context

Memory

23.7 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on RTX 4000 Ada 20GB
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: 15.8 tok/s decode · 12.2s TTFT (warm) · 40 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	30.3 tok/s	3483 ms	4K
Coding	D	Very compromised	15.8 tok/s	12221 ms	4K
Agentic Coding	F	Too heavy	6.6 tok/s	42563 ms	4K
Reasoning	D	Very compromised	15.8 tok/s	14443 ms	4K
RAG	F	Too heavy	6.6 tok/s	53204 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C46
Q3_K_S	3	5.9 GB	Low	C47
NVFP4	4	6.7 GB	Medium	C48
Q4_K_M	4	7.3 GB	Medium	C48
Q5_K_M	5	8.6 GB	High	C49
Q6_K	6	9.8 GB	High	C50
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

Opciones de mejora

Hardware que ejecuta bien StableLM 2 12B

RTX 3090 24GBOpción económica

24 GB VRAM (+4)936 GB/s (+576)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.45.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 187%.

~$1,499 MSRP

RTX 4090 24GBMejor relación calidad-precio

24 GB VRAM (+4)1008 GB/s (+648)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.53.5 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 239%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBMejora NVIDIA

24 GB VRAM (+4)672 GB/s (+312)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.46 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 191%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run StableLM 2 12B?

Yes, RTX 4000 Ada 20GB can run StableLM 2 12B with a D grade (Very compromised). Expected decode speed: 15.8 tok/s.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 23.7 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, StableLM 2 12B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12221ms using Q5_K_M quantization.

Can RTX 4000 Ada 20GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on RTX 4000 Ada 20GB receives a D grade with 15.8 tok/s and 4K context.

What context window can StableLM 2 12B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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 4000 Ada 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 4000 Ada 20GB See all hardware for StableLM 2 12B

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