Xing4.0 29B-A4B

Running Xing4.0 29B-A4B locally

At Q4_K_M and 4K of context, Xing4.0 29B-A4B needs about 21.2 GB — 19.1 GB of weights, 0.18 GB of KV cache and a 1.9 GB activation buffer. The smallest cards in this index that clear that comfortably are 32 GB ones — 4 of them, from the RTX 5090 up — and 26 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 1.23 GB, taking the total to 22.5 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 256K; holding all of it at Q4_K_M would need about 33 GB.

Which build to download

This index only tracks Xing4.0 29B-A4B in GGUF, across 9 levels (Q8_0, Q6_K, Q5_K_M, Q4_K_M, IQ4_XS, Q3_K_M, IQ3_XXS, Q2_K, IQ2_XXS). 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 Xing4.0 29B-A4B need?

About 21.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 22.5 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 Xing4.0 29B-A4B run on a 32GB GPU?

Yes — at Q4_K_M and 4K context it needs about 21.2 GB, which leaves 10.8 GB spare on a RTX 5090, or on any of the other 32 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 26 of 78 do.

Which quantization of Xing4.0 29B-A4B 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 9 levels in the index are Q8_0, Q6_K, Q5_K_M, Q4_K_M, IQ4_XS, Q3_K_M, IQ3_XXS, Q2_K, IQ2_XXS. 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.