Metis-1.6 Logos

Running Metis-1.6 Logos locally

At Q4_K_M and 4K of context, Metis-1.6 Logos needs about 7.1 GB — 6.4 GB of weights, 0.06 GB of KV cache and a 0.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 10 GB ones — 2 of them, from the Arc B570 10G up — and 67 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 0.41 GB, taking the total to 7.6 GB. That is gentler than the parameter count suggests: only 6 of 20 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 9 GB.

Which build to download

This index only tracks Metis-1.6 Logos in GGUF, across 5 levels (IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model 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 Metis-1.6 Logos need?

About 7.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 7.6 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 Metis-1.6 Logos run on a 10GB GPU?

Yes — at Q4_K_M and 4K context it needs about 7.1 GB, which leaves 2.9 GB spare on a Arc B570 10G, or on any of the other 10 GB cards here (2 in all). That is the smallest size in this index that clears it comfortably; 67 of 78 do.

Which quantization of Metis-1.6 Logos should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 5 levels in the index are IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.