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Can the NVIDIA GeForce RTX 4080 SUPER run StableLM 2 12B Chat?

67 ptsGood (B) · 67 of 100 points: Model quality 12.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 12.2 GB of 16.0 GB usable, with an estimated speed of 51.6 tok/s.

Best quant
Q6_K
Generation speed
51.6 tok/s
Prompt processing
3,096 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Download StableLM 2 12B Chat

Recommended for the NVIDIA GeForce RTX 4080 SUPER: Q6_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.

ollama run stablelm2:12b-chat-q6_K

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

Quantized files · GGUF

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

Download the Q6_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:Q6_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 7 days ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.12.5 / 45
Memory fitFull marks when everything sits in GPU or unified 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
Total67.5 / 10067 ptsGood (B) · 67 of 100 points: Model quality 12.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4080 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 7.1 GB 16.0 GB 122 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 8.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.2/15 GGUF (opens in a new tab)
Q3_K_M 8.2 GB 16.0 GB 96.2 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 10.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.8/15 GGUF (opens in a new tab)
Q4_K_M 9.6 GB 16.0 GB 74.4 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.9/15 GGUF (opens in a new tab)
Q5_K_M 10.9 GB 16.0 GB 61.5 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 12.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 12.2 GB 16.0 GB 51.6 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 12.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 15.1 GB 16.0 GB 37.9 tok/s Tight fit 66 ptsGood (B) · 66 of 100 points: Model quality 12.6/45, Memory fit 24.0/25, Speed 14.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 26.5 GB 16.0 GB 3.3 tok/s Heavy offload 35 ptsPoor (D) · 35 of 100 points: Model quality 12.6/45, Memory fit 4.0/25, Speed 4.3/15, Memory use 15.0/15 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

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.