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Can the NVIDIA GeForce RTX 4060 run Gemma 2B IT?

56 ptsUsable (C) · 56 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 3.8 GB of 8.0 GB usable, with an estimated speed of 67.7 tok/s.

Best quant
Q8_0
Generation speed
67.7 tok/s
Prompt processing
4,331 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

Download Gemma 2B IT

Recommended for the NVIDIA GeForce RTX 4060: 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 gemma:2b-instruct-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.

huggingface.co/google/gemma-2b-it (opens in a new tab)

ollama run hf.co/google/gemma-2b-it

Original weights · Hugging Face

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

huggingface.co/google/gemma-2b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the C 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.5.4 / 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.11.0 / 15
Total56.4 / 10056 ptsUsable (C) · 56 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.0/15

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

Every quantization on the NVIDIA GeForce RTX 4060

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 2.2 GB 8.0 GB 218 tok/s Runs well 49 ptsUsable (C) · 49 of 100 points: Model quality 3.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15
Q3_K_M 2.4 GB 8.0 GB 172 tok/s Runs well 51 ptsUsable (C) · 51 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15
Q4_K_M 2.7 GB 8.0 GB 133 tok/s Runs well 52 ptsUsable (C) · 52 of 100 points: Model quality 5.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.7/15
Q5_K_M 2.9 GB 8.0 GB 110 tok/s Runs well 53 ptsUsable (C) · 53 of 100 points: Model quality 5.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.5/15
Q6_K 3.2 GB 8.0 GB 92.3 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 5.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15
Q8_0 3.8 GB 8.0 GB 67.7 tok/s Runs well 56 ptsUsable (C) · 56 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.0/15
F16 6.2 GB 8.0 GB 31.2 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 5.4/45, Memory fit 25.0/25, Speed 13.2/15, Memory use 15.0/15 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.