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Can the NVIDIA GeForce RTX 5060 Ti 8GB run Gemma 4 E4B?

84 ptsGreat (A) · 84 of 100 points: Model quality 30.3/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q6_K it needs about 7.7 GB of 8.0 GB usable, with an estimated speed of 47.7 tok/s.

Best quant
Q6_K
Generation speed
47.7 tok/s
Prompt processing
2,133 tok/s
First token (1K prompt)
0.5 s
Largest safe context
26K

Download Gemma 4 E4B

Recommended for the NVIDIA GeForce RTX 5060 Ti 8GB: 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 gemma4:e4b

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/unsloth/gemma-4-E4B-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-4-E4B-it-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/google/gemma-4-E4B-it (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/google-deepmind/gemma (opens in a new tab)

git clone https://github.com/google-deepmind/gemma

Google DeepMind’s official Gemma library.

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.30.3 / 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
Total84.3 / 10084 ptsGreat (A) · 84 of 100 points: Model quality 30.3/45, 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 4.3 GB 8.0 GB 113 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.5/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 5.1 GB 8.0 GB 88.9 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 14.6/15 GGUF (opens in a new tab)
Q4_K_M 6.0 GB 8.0 GB 68.7 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 29.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 6.8 GB 8.0 GB 56.8 tok/s Tight fit 83 ptsGreat (A) · 83 of 100 points: Model quality 29.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.7 GB 8.0 GB 47.7 tok/s Tight fit 84 ptsGreat (A) · 84 of 100 points: Model quality 30.3/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 9.6 GB 8.0 GB 16.8 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 30.6/45, Memory fit 10.0/25, Speed 9.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 17.1 GB 8.0 GB 3.8 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 30.6/45, Memory fit 4.0/25, Speed 4.6/15, 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.