Can StableLM 2 12B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C54Usable

Estimated from fit model

StableLM 2 12B needs ~24.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q5_K_M quantization, expect ~41 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.9 GB, 40.8 tok/s, Runs well

24.9 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

40.8 tok/s

TTFT

4748 ms

Safe context

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 Radeon AI PRO R9700 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: 40.8 tok/s decode · 4.7s TTFT (warm) · 102 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	40.8 tok/s	2590 ms	4K
Coding	C	Runs well	40.8 tok/s	4748 ms	4K
Agentic Coding	D	Very compromised (needs ~1.2 GB host RAM)	23.0 tok/s	12249 ms	4K
Reasoning	C	Runs well	40.8 tok/s	5612 ms	4K
RAG	D	Very compromised (needs ~1.2 GB host RAM)	23.0 tok/s	15311 ms

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on Radeon AI PRO R9700 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

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

NVIDIA A100 40GBBudget pick

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 184%.115.7 tok/s decode

Raises estimated decode speed by about 184%.

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

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run StableLM 2 12B?

Yes, Radeon AI PRO R9700 32GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 40.8 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StableLM 2 12B achieves approximately 40.8 tokens per second decode speed with a time-to-first-token of 4748ms using Q5_K_M quantization.

Can Radeon AI PRO R9700 32GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on Radeon AI PRO R9700 32GB receives a C grade with 40.8 tok/s and 4K context.

What context window can StableLM 2 12B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 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.

See all results for Radeon AI PRO R9700 32GB See all hardware for StableLM 2 12B

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