Aya 23 8B

Running Aya 23 8B locally

At Q4_K_M and 4K of context, Aya 23 8B needs about 5.6 GB — 4.6 GB of weights, 0.50 GB of KV cache and a 0.5 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 76 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 3.50 GB, taking the total to 9.5 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 8K; holding all of it at Q4_K_M would need about 6 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 2 levels. Q4_K_M carries the lowest published perplexity loss at 3.2%. The fastest level listed is AWQ INT4 at about 205 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Aya 23 8B need?

About 5.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Aya 23 8B run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 5.6 GB, which leaves 2.4 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.

Which quantization of Aya 23 8B should I use?

Q4_K_M has the lowest published quality loss (3.2%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, AWQ INT4.