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Can the NVIDIA GeForce RTX 4070 Ti SUPER run Gemma 4 E4B?

85 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15

Yes, it runs well

Runs well. At Q8_0 it needs about 10.0 GB of 16.0 GB usable, with an estimated speed of 52.5 tok/s.

Best quant
Q8_0
Generation speed
52.5 tok/s
Prompt processing
3,969 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Download Gemma 4 E4B

Recommended for the NVIDIA GeForce RTX 4070 Ti SUPER: 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 gemma4:e4b-it-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/unsloth/gemma-4-E4B-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-4-E4B-it-GGUF:Q8_0

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 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.14.4 / 15
Total85.0 / 10085 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15

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

Every quantization on the NVIDIA GeForce RTX 4070 Ti SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.7 GB 16.0 GB 169 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 5.5 GB 16.0 GB 133 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.9/15 GGUF (opens in a new tab)
Q4_K_M 6.4 GB 16.0 GB 103 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 29.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15 GGUF (opens in a new tab)
Q5_K_M 7.2 GB 16.0 GB 85.2 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.4/15 GGUF (opens in a new tab)
Q6_K 8.1 GB 16.0 GB 71.5 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 30.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15 GGUF (opens in a new tab)
Q8_0 10.0 GB 16.0 GB 52.5 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 14.4/15 GGUF (opens in a new tab)
F16 17.5 GB 16.0 GB 12.9 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 30.6/45, Memory fit 10.0/25, Speed 8.5/15, 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.