Can I run Gemma 4 31b on an RTX 5080 (16GB)?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-10-16.
Memory breakdown (Q4_K_M, F16 KV, 33K context)
| Model weights | 17.5 GB |
| KV cache | 3.3 GB |
| Runtime overhead + reserve | 5.6 GB |
| Total used | 26.3 / 16 GB |
| Short by | 10.3 GB |
Max context that fits at Q4_K_M: does not fit.
Every quantization on the RTX 5080
| Weight quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| Q4_K_M | 17.5 GB | ❌ won't fit | 26.3 / 16.0 GB |
| Q5_K_M | 20.4 GB | ❌ won't fit | 29.6 / 16.0 GB |
| Q6_K | 23.5 GB | ❌ won't fit | 33.0 / 16.0 GB |
| Q8_0 | 30.4 GB | ❌ won't fit | 40.8 / 16.0 GB |
| FP16 | 57.2 GB | ❌ won't fit | 70.8 / 16.0 GB |
Lower weight quants free memory at some output-quality cost — Q4 is the common sweet spot; below that quality drops faster.
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Embed this verdict
Live badge for your README or model card — recomputed by the engine, never stale:
[](https://fitllm.run/can-i-run/gemma-4-31b-on-rtx-5080)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm-engine "Gemma 4 31b" --gpu "RTX 5080"Why most VRAM calculators get this wrong
Gemma 4 31b interleaves sliding-window (local) and global attention 5:1. The local layers cap their KV cache at the 1024-token window, and the global layers use a different head shape (head_dim 512 vs 256). A naive "all layers × full context × one head_dim" formula over-counts KV cache by several times.
What fits on the RTX 5080 instead
same GPU Models that fit on the RTX 5080: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, Spark-X2.5-4B, MiniCPM5-2B.
same model GPUs that run Gemma 4 31b: RTX 5090 (32GB), RTX 6000 Ada (48GB), RTX PRO 6000 Blackwell (96GB), Radeon PRO W7900 (48GB), 2× RTX 3090 (48GB), 2× RTX 4090 (48GB), 4× RTX 3090 (96GB), A100 40GB (40GB), A100 80GB (80GB), H100 80GB (80GB), H200 141GB (141GB), B200 (180GB), RTX PRO 5000 Blackwell 48GB (48GB), RTX PRO 5000 Blackwell 72GB (72GB), RTX PRO 4500 Blackwell (32GB), DGX Spark 128GB (128GB), 2× DGX Spark 128GB (256GB), 4× DGX Spark 128GB (512GB).
Reproduce it
Gemma 4 31b = 30.7B, 60 layers. The RTX 5080 has 16GB / 960GB/s. Same math, open source: fitllm-engine. GGUF bpw from llama.cpp.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home