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Can the NVIDIA GeForce RTX 5080 Laptop GPU run Granite 4.2 8B?

85 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 12.1 GB of 16.0 GB usable, with an estimated speed of 63.7 tok/s.

Best quant
Q8_0
Generation speed
63.7 tok/s
Prompt processing
3,358 tok/s
First token (1K prompt)
0.3 s
Largest safe context
31K

Download Granite 4.2 8B

Recommended for the NVIDIA GeForce RTX 5080 Laptop GPU: Q8_0. 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 granite4.2:8b-q8_0

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 Q8_0 file (opens in a new tab)

huggingface.co/ibm-granite/granite-4.2-8b-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-4.2-8b-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-4.2-8b (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-4.2-language-models (opens in a new tab)

git clone https://github.com/ibm-granite/granite-4.2-language-models

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

How the S 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.30.6 / 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
Total85.6 / 10085 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, 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 5080 Laptop GPU

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.3 GB 16.0 GB 205 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.0/15 GGUF (opens in a new tab)
Q3_K_M 7.0 GB 16.0 GB 162 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.2/15 GGUF (opens in a new tab)
Q4_K_M 8.1 GB 16.0 GB 125 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 29.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15 GGUF (opens in a new tab)
Q5_K_M 9.0 GB 16.0 GB 103 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.0/15 GGUF (opens in a new tab)
Q6_K 9.9 GB 16.0 GB 86.8 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 30.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15 GGUF (opens in a new tab)
Q8_0 12.1 GB 16.0 GB 63.7 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 20.3 GB 16.0 GB 7.2 tok/s Runs with offload 61 ptsGood (B) · 61 of 100 points: Model quality 30.6/45, Memory fit 10.0/25, Speed 6.4/15, Memory use 15.0/15 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.