Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026
Yes. Gemma 4 E2B needs 2.9 GB at Q4 (QAT) and the NVIDIA GeForce RTX 3090 has 24 GB, leaving about 21.1 GB of headroom, at roughly 124 tok/s (estimated) with room for up to 128K of context.
See what else this hardware can run →
| Model | Gemma 4 E2B |
| Parameters | 2B (Efficient) |
| Quantization | Q4 (QAT) |
| VRAM needed | 2.9 GB |
| NVIDIA GeForce RTX 3090 VRAM | 24 GB |
| Verdict | Runs |
| Max context | 128K |
| Estimated speed | ~124 tok/s |
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 11.5 GB | ✓ Yes | 64K | — |
| Q8_0 | 6.7 GB | ✓ Yes | 64K | — |
| Q6_K | 5.5 GB | ✓ Yes | 64K | — |
| Q5_K_M | 4.9 GB | ✓ Yes | 64K | — |
| Q4_K_M | 4.4 GB | ✓ Yes | 64K | — |
| Q3_K_M | 3.5 GB | ✓ Yes | 64K | — |
| Q2_K | 3 GB | ✓ Yes | 64K | — |
Assumes a 4K-token context with an f16 KV cache, on an estimated architecture — this model publishes no config we can read. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 24 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4 (QAT) on the NVIDIA GeForce RTX 3090: 8K 4.7 GB · 32K 7.7 GB · 125K 19.4 GB. Every window this model publishes still fits 24 GB.
Nothing to change — run it at Q4 (QAT). If you want more capability, check whether the next size up also fits.
No compatible GPU? Gemma 4 E2B on 8 GB of system RAM, CPU only: runs, but it is tight.
Running Gemma 4 E2B on this card:
At 2 hrs/day, buying (~$1,499) beats renting at $0.34/hr after about 6.1 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
All Gemma 4 sizes on the NVIDIA GeForce RTX 3090 | Gemma 4 E2B model page | NVIDIA GeForce RTX 3090 GPU page | Check your hardware