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Can the NVIDIA GeForce RTX 5060 Ti 8GB run Granite 4.2 8B?

83 ptsGreat (A) · 83 of 100 points: Model quality 29.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 7.7 GB of 8.0 GB usable, with an estimated speed of 62.6 tok/s.

Best quant
Q4_K_M
Generation speed
62.6 tok/s
Prompt processing
1,941 tok/s
First token (1K prompt)
0.5 s
Largest safe context
9K

Download Granite 4.2 8B

Recommended for the NVIDIA GeForce RTX 5060 Ti 8GB: Q4_K_M. 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-q4_K_M

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 Q4_K_M 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:Q4_K_M

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 5 days ago against Hugging Face, Ollama and GitHub.

How the A 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.29.1 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total83.1 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 29.1/45, Memory fit 24.0/25, 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 5060 Ti 8GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.9 GB 8.0 GB 103 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 6.6 GB 8.0 GB 80.9 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 7.7 GB 8.0 GB 62.6 tok/s Tight fit 83 ptsGreat (A) · 83 of 100 points: Model quality 29.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 8.6 GB 8.0 GB 33.7 tok/s Runs with offload 68 ptsGood (B) · 68 of 100 points: Model quality 29.7/45, Memory fit 10.0/25, Speed 13.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 9.5 GB 8.0 GB 19.5 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 30.3/45, Memory fit 10.0/25, Speed 10.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 11.7 GB 8.0 GB 9.8 tok/s Heavy offload 57 ptsUsable (C) · 57 of 100 points: Model quality 30.6/45, Memory fit 4.0/25, Speed 7.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 19.9 GB 8.0 GB 3.0 tok/s Heavy offload 53 ptsUsable (C) · 53 of 100 points: Model quality 30.6/45, Memory fit 4.0/25, Speed 4.1/15, 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.