New training-free or low-bit methods such as PRQuant, Phantom-kv, RBS-Attention and Elastic Threshold Attention reduce memory, token and prefill overhead. Ternary and small low-bit models shrink footprint, but FP4 on small dense models remains risky.
PRQuant combines channel reorganization and static residual compensation to reduce inference overhead in low-bit quantization. The method is training-free, builds contiguous tail blocks offline, and avoids dynamic online gathering by turning scattere...
A system called phantom-kv injects a small bank of key/value tensors into a model's KV cache. The result is a per-request capability mode: load it, use it, unload it. The weights remain identical. The audit, however, highlights semantic limits and a ...
An end-to-end trainable architecture replaces rigid heuristics with dynamic contextual thresholds, reducing KV cache traffic without sacrificing quality. The 1.45 billion parameter model achieves up to 2.5x faster decoding than FlashAttention-2 on 51...
A training-free method reduces prefill cost in long-context LLMs by targeting 'mean dilution', where a block centroid hides relevant tokens. RBS-Attention combines a centroid-based branch with a rescue branch based on maximum key-block radius. On H10...
A post on r/ROCm highlights the problem of FP4-optimized inference engines: fast in benchmarks, but often unreliable on small dense models. Compression can work on Large Language Models with many parameters, but on small models logical errors become ...
UkisAI's question about Bonsai 2 Swift is not a simple update request. It highlights how the TCO of local LLMs increasingly depends on runtime behavior: wasted tokens, overthinking loops and quantization choices are redefining on-premise efficiency.
After Swift Qwen3.8 27B downloads jumped from 100,000 to 150,000 overnight, UkisAI is asking whether to apply the same token-efficiency treatment to Bonsai 2, which their tests show suffers from overthinking loops and high token usage. The team wants...
A layer-wise curriculum learning method for LLM compression starts with easy optimization tasks and moves to harder ones, stabilizing knowledge transfer. It reduces GPU memory usage and training hours by more than 50% on BERT and GPT-2, and outperfor...
Ternary Bonsai 2 is available on Hugging Face: it derives from Qwen3.8-27B, keeps the hybrid-attention architecture, and uses ternary weights to shrink below 6GB. According to the model card, it is 9x smaller than FP16 while retaining 98.2% of the in...
UkisAI's open-source model passes 100k downloads and ranks first among fine-tunes on Hugging Face. It penalizes overthinking patterns in small LLMs, cutting token usage by 58.3% and boosting speed 1.95x without accuracy loss. Swift1.5 and Swift Qwen3...
ToMoE turns dense checkpoints into MoE without fine-tuning, lowering active parameters per token without reducing memory footprint. A shift for on-premise deployments: less compute, same model. Runtime, latency, and VRAM constraints remain. The signa...
KVBoost reuses key-value cache at chunk level and reduces time-to-first-token by 4.49x on Qwen2.5-3B, with no accuracy loss. Unlike prefix caching, it does not require a shared leading prefix: a dual hash separates positional and content identity. As...
A new method converts dense LLMs into Mixture-of-Experts models through differentiable dynamic structural pruning, reducing active parameters per token without removing them. It needs no fine-tuning and outperforms previous structural pruning on Phi-...
llama.cpp enables MTP for GLM-4.5-Air, a 106-billion-parameter MoE with only 12 billion active parameters. The change speeds up inference on memory-rich but compute-limited machines like Strix Halo, DGX Spark, and RTX 3090, and strengthens the fine-t...
A single developer reduced Qwen3.8-27B to 22.7 billion parameters with depth pruning, no fine-tuning. Distributed only as MLX for Apple Silicon, the model shows trade-offs: lower memory and compute pressure, but losses on edge cases. For on-premise d...
ECASQ jointly optimizes adaptive quantization and lossless compression by minimizing MSE under an entropy budget and an unbiasedness constraint. The optimal dynamic program runs in O(sd^2) time and O(d^2) space. A GPU-friendly approximate version red...
A developer applied depth pruning to Qwen3.8-27B, bringing it to roughly 22.7 billion parameters without fine-tuning. The model, available in bf16, q8, and q4 on MLX, handles coding, agentic use, and multi-turn conversations with limited degradation,...
