Can I run it?
Can I run gemma 4 E4B it on GeForce RTX 3080 10GB?
Tight fit
Tight fit fully on your GeForce RTX 3080 10GB at UD-Q8_K_XL, 8K context, about 44–61 tok/s. Weights 8.11 GB plus 0.14 GB of KV cache — 8.64 GB of 9.03 GB usable. Up to 16K context still fits.
Best quantUD-Q8_K_XL
Working context8K
Max that fits16K
Speed44–61 tok/sEstimate from memory bandwidth, not a benchmark. Method.
Weights8.11 GB
KV cache0.14 GB
Total8.64 GB
Usable9.03 GB
gemma 4 E4B it details · GeForce RTX 3080 10GB page · How this estimate is made
Run commands
llama.cpp
llama-server -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q8_K_XL -c 8192 -ngl 42
Ollama
ollama run gemma
LM Studio
lms get unsloth/gemma-4-E4B-it-GGUF # Then load quant UD-Q8_K_XL
Assumptions
- Estimate from memory bandwidth, not a benchmark. Decode speed uses 55–75% of peak bandwidth.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- UD-Q8_K_XL size is the real GGUF file size from Hugging Face.
- 0.5 GB is reserved because this GPU also drives a display, then 5% of the rest is left free.
- Speed is quoted at 8K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on GeForce RTX 3080 10GB? — Heavily degraded, UD-IQ2_S, 47–64 tok/s (estimate)
- Can I run gemma 4 12b it on GeForce RTX 3080 10GB? — Tight fit, Q5_K_M, 42–57 tok/s (estimate)
- Can I run Qwen3.5 9B on GeForce RTX 3080 10GB? — Tight fit, UD-Q6_K_XL, 46–63 tok/s (estimate)
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.