StableLM 2 12B needs ~23.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q5_K_M quantization, expect ~27 tok/s.
Operating mode
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.
Select quantization to explore
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.4 GB host RAM)
Decode
27.1 tok/s
TTFT
7149 ms
Safe context
4K
Memory
23.7 GB / 20.0 GB
Offload
20%
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 51.8 tok/s | 2038 ms | 4K |
| Coding | D | Very compromised | 27.1 tok/s | 7149 ms | 4K |
| Agentic Coding | F | Too heavy | 11.3 tok/s | 24899 ms | 4K |
| Reasoning | D | Very compromised | 27.1 tok/s | 8449 ms | 4K |
| RAG | F | Too heavy | 11.3 tok/s | 31124 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on RX 7900 XT 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 |
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 99Upgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 76%.
~$999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 51%.
~$1,899 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$2,249 MSRP
Yes, RX 7900 XT 20GB can run StableLM 2 12B with a D grade (Very compromised). Expected decode speed: 27.1 tok/s.
StableLM 2 12B (12B parameters) requires approximately 23.7 GB of memory with Q5_K_M quantization.
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
On RX 7900 XT 20GB, StableLM 2 12B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7149ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on RX 7900 XT 20GB receives a D grade with 27.1 tok/s and 4K context.
On RX 7900 XT 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.