StableLM 2 12B Chat on AMD Radeon RX 9060 XT 8GB

Can the AMD Radeon RX 9060 XT 8GB run StableLM 2 12B Chat?

Yes, it runs great

Runs great. At Q2_K it needs about 6.7 GB of 8.0 GB usable, with an estimated speed of 45.8 tok/s.

Best quant: Q2_K
Generation speed: 45.8 tok/s
Prompt processing: 2,615 tok/s
First token (1K prompt): 0.4 s
Largest safe context: 4K

Best app to run it on the AMD Radeon RX 9060 XT 8GB

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

lemonade pull stabilityai/stablelm-2-12b-chat-GGUF:Q2_K

LM Studio Point-and-click: search, download and chat in one window.
Ollama One command with Ollama’s own build of this model.

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

lemonade pull stabilityai/stablelm-2-12b-chat-GGUF:Q2_K

LM Studio Point-and-click: search, download and chat in one window.
Ollama One command with Ollama’s own build of this model.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download StableLM 2 12B Chat

Recommended for the AMD Radeon RX 9060 XT 8GB: Q2_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run stablelm2:12b-chat-q2_K

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K file (opens in a new tab)

huggingface.co/stabilityai/stablelm-2-12b-chat-GGUF (opens in a new tab)

ollama run hf.co/stabilityai/stablelm-2-12b-chat-GGUF:Q2_K

Original weights · Hugging Face

Full-precision files from Stability AI, for fine-tuning, vLLM, Transformers.

huggingface.co/stabilityai/stablelm-2-12b-chat (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great: How well it runs on the AMD Radeon RX 9060 XT 8GB: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	8.8 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Match score	63.8 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Radeon RX 9060 XT 8GB

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	6.7 GB	8.0 GB	45.8 tok/s	Runs great	19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	7.8 GB	8.0 GB	36.0 tok/s	Runs welltight fit	23Basic quality (D) · 23 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	9.2 GB	8.0 GB	18.3 tok/s	Runs welloffloads to RAM	26Basic quality (D) · 26 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	10.5 GB	8.0 GB	12.2 tok/s	Runs welloffloads to RAM	27Basic quality (D) · 27 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	11.8 GB	8.0 GB	8.8 tok/s	Slowheavy RAM offload	27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	14.7 GB	8.0 GB	5.4 tok/s	Slowheavy RAM offload	28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 37/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	26.1 GB	8.0 GB	2.0 tok/s	Very slowheavy RAM offload	28Basic quality (D) · 28 of 100: how capable the model is at F16, whatever it runs on. Match score 34/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q8_0 · 42.6 tok/s · Runs great
28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q8_0 · 48.2 tok/s · Runs great
28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q8_0 · 51.9 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q6_K · 47.1 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q6_K · 51.6 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q6_K · 62.9 tok/s · Runs great

Quick answers

How much memory does StableLM 2 12B Chat need on the RX 9060 XT 8GB?

At Q2_K it needs about 6.7 GB. The RX 9060 XT 8GB has 8.0 GB usable.

How fast is StableLM 2 12B Chat on the RX 9060 XT 8GB?

About 45.8 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of StableLM 2 12B Chat should I use on the RX 9060 XT 8GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.