Smaller alternatives
Models that need less memory than Gemma 4 E2B and score about as well or better.
No scored model is both smaller and about as intelligent as Gemma 4 E2B.
Gemma 4 E2B
Vision
Google · 5.1B · Dense
Gemma 4 E2B is a 5.1B model from Google with a 128K-token context. At Q4_K_M with an 8K context it needs about 3.3 GB; 6 GB leaves room for longer chats.
Quantization options
| Quant | Bits | Memory | Quality | On your hardware |
|---|---|---|---|---|
| Q2_K | 3.16 | — | Noticeably worse | … |
| Q3_K_M | 4 | — | Some loss | … |
| Q4_K_M | 4.89 | — | Good | … |
| Q5_K_M | 5.7 | — | Good | … |
| Q6_K | 6.56 | — | Near original | … |
| Q8_0 | 8.5 | — | Near original | … |
| F16 | 16 | — | Original | … |
Memory by context length
At Q4_K_M. The context cache grows with every token the model keeps in mind.
| Context | Context cache | Total |
|---|---|---|
| 4K | 0 GB | 3.2 GB |
| 8K | 0.1 GB | 3.3 GB |
| 32K | 0.2 GB | 3.4 GB |
| 128K | 0.9 GB | 4.1 GB |
Next steps
Compare it with
Scored models of a similar size, side by side on your device.
Least hardware that runs it well
Desktop devices with the least memory that give Gemma 4 E2B grade A or S at Q4_K_M.
Can I run Gemma 4 E2B locally?
Can I run Gemma 4 E2B locally?
Yes, if your GPU or Mac has about 3.3 GB free for it at Q4_K_M. Open this page on that computer to see the grade for your exact hardware, then start it with llama.cpp, Ollama or LM Studio.
How much memory does Gemma 4 E2B need?
About 3.3 GB at Q4_K_M with an 8K context and 5.1 GB at Q8_0. Lower quantizations fit smaller cards with some loss in quality; longer contexts add to the total.