Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-06.
| Model weights | 14.5 GB |
| KV cache | 0.8 GB |
| Runtime overhead + reserve | 4.8 GB |
| Total used | 20.2 / 16 GB |
| Short by | 4.2 GB |
Max context that fits at Q4_K_M: does not fit.
| Weight quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| Q4_K_M | 14.5 GB | ❌ won't fit | 20.2 / 16.0 GB |
| Q5_K_M | 16.9 GB | ❌ won't fit | 22.9 / 16.0 GB |
| Q6_K | 19.5 GB | ❌ won't fit | 25.7 / 16.0 GB |
| Q8_0 | 25.2 GB | ❌ won't fit | 32.2 / 16.0 GB |
| FP16 | 47.5 GB | ❌ won't fit | 57.1 / 16.0 GB |
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Gemma 4 26b A4B 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.
same GPU Models that fit on the RTX 5080: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b.
same model GPUs that run Gemma 4 26b A4B: RTX 5090 (32GB), RTX 4090 (24GB), RTX 3090 (24GB), RTX 3090 Ti (24GB).
Gemma 4 26b A4B = 25.5B (4B active, MoE), 30 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