Can the Apple M2 (8 GB) run Gemma 4 E2B?

77 pts

Yes, it runs well

Runs well. At Q4_K_M it needs about 4.2 GB of 5.4 GB usable, with an estimated speed of 20.6 tok/s.

Best quant: Q4_K_M
Generation speed: 20.6 tok/s
Prompt processing: 436 tok/s
First token (1K prompt): 2.3 s
Largest safe context: 128K

Download Gemma 4 E2B

Recommended for the Apple M2 (8 GB): 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 gemma4:e2b-it-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/unsloth/gemma-4-E2B-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-4-E2B-it-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/google/gemma-4-E2B-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.	26.5 / 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.	10.8 / 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
Total	77.3 / 100	77 ptsGreat (A) · 77 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 10.8/15, 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 Apple M2 (8 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	3.1 GB	5.4 GB	33.9 tok/s	Runs well	71 ptsGood (B) · 71 of 100 points: Model quality 19.5/45, Memory fit 25.0/25, Speed 13.8/15, Memory use 13.5/15	GGUF (UD-Q2_K_XL) (opens in a new tab)
`Q3_K_M`	3.6 GB	5.4 GB	26.7 tok/s	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 12.3/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q4_K_M`	4.2 GB	5.4 GB	20.6 tok/s	Runs well	77 ptsGreat (A) · 77 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 10.8/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q5_K_M`	4.7 GB	5.4 GB	17.1 tok/s	Tight fit	75 ptsGreat (A) · 75 of 100 points: Model quality 27.1/45, Memory fit 24.0/25, Speed 9.8/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q6_K`	5.3 GB	5.4 GB	14.3 tok/s	Tight fit	75 ptsGreat (A) · 75 of 100 points: Model quality 27.6/45, Memory fit 24.0/25, Speed 9.0/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q8_0`	6.5 GB	5.4 GB	8.9 tok/s	Heavy offload	53 ptsUsable (C) · 53 of 100 points: Model quality 27.9/45, Memory fit 4.0/25, Speed 7.1/15, Memory use 15.0/15	GGUF (opens in a new tab)
`F16`	11.3 GB	5.4 GB	—	Won't run	8 ptsWon't run (F) · 8 of 100 points: Model quality 8.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	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

82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER F16 · 41.4 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti F16 · 50.4 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080 F16 · 54.0 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060 Q6_K · 45.2 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 Intel Arc B580 Q8_0 · 42.5 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 NVIDIA GeForce RTX 3060 12GB Q8_0 · 43.9 tok/s

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.