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Can the NVIDIA GeForce RTX 4080 SUPER run Granite 3.3 2B?

63 ptsGood (B) · 63 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15

Yes, it runs well

Runs well. At F16 it needs about 7.1 GB of 16.0 GB usable, with an estimated speed of 83.8 tok/s.

Best quant
F16
Generation speed
83.8 tok/s
Prompt processing
14,855 tok/s
First token (1K prompt)
0.3 s
Largest safe context
113K

Download Granite 3.3 2B

Recommended for the NVIDIA GeForce RTX 4080 SUPER: F16. 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 granite3.3:2b

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 F16 file (opens in a new tab)

huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-3.3-2b-instruct-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-3.3-2b-instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from IBM. The weights themselves download from Hugging Face.

github.com/ibm-granite/granite-3.3-language-models (opens in a new tab)

git clone https://github.com/ibm-granite/granite-3.3-language-models

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.13.5 / 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.10.3 / 15
Total63.8 / 10063 ptsGood (B) · 63 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15

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 3.1 GB 16.0 GB 586 tok/s Runs well 53 ptsUsable (C) · 53 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 GGUF (opens in a new tab)
Q3_K_M 3.3 GB 16.0 GB 462 tok/s Runs well 56 ptsUsable (C) · 56 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15 GGUF (opens in a new tab)
Q4_K_M 3.6 GB 16.0 GB 357 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.2/15 GGUF (opens in a new tab)
Q5_K_M 3.9 GB 16.0 GB 295 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (opens in a new tab)
Q6_K 4.1 GB 16.0 GB 248 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15 GGUF (opens in a new tab)
Q8_0 4.8 GB 16.0 GB 182 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15 GGUF (opens in a new tab)
F16 7.1 GB 16.0 GB 83.8 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K 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.