Can StableLM 2 12B run on RTX 4090 24GB?

YES — With Offload

C52Usable

Estimated from fit model

StableLM 2 12B needs ~24.1 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q5_K_M quantization, expect ~54 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.1 GB, 53.5 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

53.5 tok/s

TTFT

3622 ms

Safe context

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 4090 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: 53.5 tok/s decode · 3.6s TTFT (warm) · 134 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	72.2 tok/s	1463 ms	4K
Coding	C	Runs with offload (needs ~0.1 GB host RAM)	53.5 tok/s	3622 ms	4K
Agentic Coding	F	Too heavy	22.6 tok/s	12465 ms	4K
Reasoning	C	Runs with offload (needs ~0.1 GB host RAM)	53.5 tok/s	4280 ms	4K
RAG	F	Too heavy	22.6 tok/s	15582 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on RTX 4090 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

Upgrade options

Hardware that runs StableLM 2 12B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+784)

Raises estimated decode speed by about 93%.103.1 tok/s decode

Raises estimated decode speed by about 93%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)

Raises estimated decode speed by about 39%.74.2 tok/s decode

Raises estimated decode speed by about 39%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.49.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can RTX 4090 24GB run StableLM 2 12B?

Yes, RTX 4090 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 53.5 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 4090 24GB?

On RTX 4090 24GB, StableLM 2 12B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q5_K_M quantization.

Can RTX 4090 24GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on RTX 4090 24GB receives a C grade with 53.5 tok/s and 4K context.

What context window can StableLM 2 12B use on RTX 4090 24GB?

On RTX 4090 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 4090 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4090 24GB See all hardware for StableLM 2 12B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

72.2 tok/s

1463 ms

Coding

Runs with offload (needs ~0.1 GB host RAM)

53.5 tok/s

3622 ms

Agentic Coding

Too heavy

22.6 tok/s

12465 ms

Reasoning

Runs with offload (needs ~0.1 GB host RAM)

53.5 tok/s

4280 ms

RAG

Too heavy

22.6 tok/s

15582 ms

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on RTX 4090 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

Frequently asked questions

Can RTX 4090 24GB run StableLM 2 12B?

Yes, RTX 4090 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 53.5 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 4090 24GB?

On RTX 4090 24GB, StableLM 2 12B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q5_K_M quantization.

Can RTX 4090 24GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on RTX 4090 24GB receives a C grade with 53.5 tok/s and 4K context.

What context window can StableLM 2 12B use on RTX 4090 24GB?

On RTX 4090 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 4090 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.