Can StableLM 2 12B run on NVIDIA A10 24GB?

Q: What should I upgrade first if StableLM 2 12B feels slow on NVIDIA A10 24GB?

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

YES — With Offload

C51Usable

Estimated from fit model

StableLM 2 12B needs ~24.1 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q5_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.1 GB, 37.4 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

37.4 tok/s

TTFT

5174 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 NVIDIA A10 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: 37.4 tok/s decode · 5.2s TTFT (warm) · 94 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	C	Runs well	50.5 tok/s	2090 ms	4K
Coding	C	Runs with offload (needs ~0.1 GB host RAM)	37.4 tok/s	5174 ms	4K
Agentic Coding	F	Too heavy	15.8 tok/s	17808 ms	4K
Reasoning	C	Runs with offload	37.4 tok/s	6115 ms	4K
RAG	F	Too heavy	15.8 tok/s	22260 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on NVIDIA A10 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

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 (+1192)

Raises estimated decode speed by about 176%.103.1 tok/s decode

Raises estimated decode speed by about 176%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+296)

Raises estimated decode speed by about 98%.74.2 tok/s decode

Raises estimated decode speed by about 98%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Raises estimated decode speed by about 33%.49.8 tok/s decode

Raises estimated decode speed by about 33%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA A10 24GB run StableLM 2 12B?

Yes, NVIDIA A10 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 37.4 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, StableLM 2 12B achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5174ms using Q5_K_M quantization.

Can NVIDIA A10 24GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on NVIDIA A10 24GB receives a C grade with 37.4 tok/s and 4K context.

What context window can StableLM 2 12B use on NVIDIA A10 24GB?

On NVIDIA A10 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 NVIDIA A10 24GB?

See all results for NVIDIA A10 24GB See all hardware for StableLM 2 12B

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