Can I Run Gemma 3n on NVIDIA GeForce RTX 3080 (10GB)?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, comfortably — Gemma 3n E4B at Q4_K_M needs 6.7 GB at 8K context (4.7 GB weights + 2 GB KV cache/overhead), leaving ~3.3 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~119 tok/s (est.), with room for up to 16K context.
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NVIDIA GeForce RTX 3080 (10GB) Specs
| VRAM | 10 GB |
| Memory Bandwidth | 760 GB/s |
Gemma 3n E4B on the NVIDIA GeForce RTX 3080 (10GB): VRAM by quantization
| Quant | VRAM needed | Fits 10 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 17.6 GB | ✗ No | — | — |
| Q8_0 | 10.3 GB | ✗ No | — | — |
| Q6_K | 8.4 GB | ✓ Yes | 16K | — |
| Q5_K_M | 7.5 GB | ✓ Yes | 16K | — |
| Q4_K_M | 6.7 GB | ✓ Yes | 16K | — |
| Q3_K_M | 5.3 GB | ✓ Yes | 32K | — |
| Q2_K | 4.5 GB | ✓ Yes | 32K | — |
Assumes an 8K-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 10 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_K_M on the NVIDIA GeForce RTX 3080 (10GB): 8K 6.7 GB · 32K 10.1 GB ✗. Past 32K it no longer fits 10 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Gemma 3n E4B on 16 GB of system RAM, CPU only: runs.
Gemma 3n Sizes That Fit the NVIDIA GeForce RTX 3080 (10GB) (at 8K context)
| Gemma 3n E4B | Q4_K_M · 6.7 GB at 8K context · ~119 tok/s (est.) |
| Gemma 3n E2B | Q4_K_M · 5.1 GB at 8K context · ~174 tok/s (est.) |
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FAQ
Will Gemma 3n run on the NVIDIA GeForce RTX 3080 (10GB)?
Yes, comfortably — Gemma 3n E4B at Q4_K_M needs 6.7 GB at 8K context (4.7 GB weights + 2 GB KV cache/overhead), leaving ~3.3 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~119 tok/s (est.), with room for up to 16K context.
Which Gemma 3n variant fits best on the NVIDIA GeForce RTX 3080 (10GB)?
Gemma 3n E4B at Q4_K_M quantization needs 6.7 GB at 8K context (4.7 GB weights + 2 GB KV cache/overhead), estimated ~119 tokens/sec, up to 16K context.
Every Gemma 3n size on the NVIDIA GeForce RTX 3080 (10GB)
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| Gemma 3n E4B | 6.7 GB | Runs | ~119 tok/s |
| Gemma 3n E2B | 5.1 GB | Runs | ~174 tok/s |
Gemma 3n on Other GPUs
- Gemma 3n on Intel Arc B570
- Gemma 3n on NVIDIA GeForce RTX 5060 Ti 8GB
- Gemma 3n on NVIDIA GeForce RTX 5060
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