Kolibri-1 78B-A3.5B

Running Kolibri-1 78B-A3.5B locally

At Q4_K_M and 4K of context, Kolibri-1 78B-A3.5B needs about 52.2 GB — 47.3 GB of weights, 0.18 GB of KV cache and a 4.8 GB activation buffer. The smallest card in this index that clears that comfortably is the DGX Spark 64G at 64 GB, and 12 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 0.54 GB, taking the total to 52.8 GB. That is gentler than the parameter count suggests: only 10 of 50 layers keep a KV cache that grows with context, and 40 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 256K; holding all of it at Q4_K_M would need about 58 GB.

Which build to download

This index only tracks Kolibri-1 78B-A3.5B in GGUF, across 8 levels (IQ1_M, IQ2_XXS, IQ2_XS, IQ3_XXS, Q3_K_M, IQ4_XS, Q4_K_M, 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 Kolibri-1 78B-A3.5B need?

About 52.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 52.8 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 Kolibri-1 78B-A3.5B run on a 64GB GPU?

Yes — at Q4_K_M and 4K context it needs about 52.2 GB, which leaves 11.8 GB spare on a DGX Spark 64G. That is the smallest card in this index that clears it comfortably; 12 of 78 do.

Which quantization of Kolibri-1 78B-A3.5B 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 8 levels in the index are IQ1_M, IQ2_XXS, IQ2_XS, IQ3_XXS, Q3_K_M, IQ4_XS, Q4_K_M, 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.