Can StableLM 2 12B run on RX 7900 XTX 24GB?

YES — With Offload

C52Usable

Estimated from fit model

StableLM 2 12B needs ~24.1 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q5_K_M quantization, expect ~48 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, 47.8 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

47.8 tok/s

TTFT

4054 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 RX 7900 XTX 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: 47.8 tok/s decode · 4.1s TTFT (warm) · 119 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	64.5 tok/s	1637 ms	4K
Coding	C	Runs with offload (needs ~0.1 GB host RAM)	47.8 tok/s	4054 ms	4K
Agentic Coding	F	Too heavy	20.2 tok/s	13951 ms	4K
Reasoning	C	Runs with offload (needs ~0.1 GB host RAM)	47.8 tok/s	4791 ms	4K
RAG	F	Too heavy	20.2 tok/s	17439 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on RX 7900 XTX 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

Radeon AI PRO R9700 32GB经济之选

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.40.8 tok/s 解码

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

~$1,899 MSRP

Radeon Pro W6800 32GB最佳性价比

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.31 tok/s 解码

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

~$2,249 MSRP

Radeon Pro W7800 32GBAMD 升级

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.36.7 tok/s 解码

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

~$2,499 MSRP

Frequently asked questions

Can RX 7900 XTX 24GB run StableLM 2 12B?

Yes, RX 7900 XTX 24GB can run StableLM 2 12B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 47.8 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 RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, StableLM 2 12B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4054ms using Q5_K_M quantization.

Can RX 7900 XTX 24GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on RX 7900 XTX 24GB receives a C grade with 47.8 tok/s and 4K context.

What context window can StableLM 2 12B use on RX 7900 XTX 24GB?

On RX 7900 XTX 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 RX 7900 XTX 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 RX 7900 XTX 24GB See all hardware for StableLM 2 12B

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