Show HN: UATC-Closed-loop VRAM control and dynamic data pruning for LLM training
A GitHub project introduces UATC, a closed-loop VRAM control and dynamic data pruning system for LLM training. The tool aims to optimize memory usage and training efficiency by dynamically adjusting VRAM allocation and pruning data during training.
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Show HN: Latent-free ternary LLM training
A GitHub project introduces a method for training ternary LLMs without latent states, potentially reducing memory and computation. The approach is shared on Hacker News for community feedback.
Two papers propose token-adaptive KV cache compression for long-context LLMs
Two arXiv papers from July 7, 2026 introduce token-adaptive KV cache compression methods for long-context LLM inference. DepthWeave-KV factorizes key/value states across neighboring layers using shared low-rank bases with token-specific residuals. FreqDepthKV uses shared low-frequency depth components and sparse high-frequency residuals, with an online probe assigning attention heads to different cache modes. Both aim to reduce memory bandwidth while preserving retrieval and reasoning quality.
Super-Tuning: Activation-Aware Pruning Reused for Sparse Fine-Tuning
A new arxiv paper proposes Super, a sparse PEFT method that reuses Wanda-style activation-weighted magnitude scores from pruning to select a small trainable support, and Supra, a hybrid adapter combining sparse updates with LoRA. This approach reduces memory and compute for fine-tuning LLMs while maintaining performance.
Study evaluates energy, performance, and accuracy trade-offs across vLLM configurations
A new arxiv paper presents a large-scale controlled study of three vLLM configuration options—attention kernel type, prefix caching, and chunked prefill—examining their impact on energy consumption, performance, and output quality. The work addresses a gap in understanding how inference engine configuration affects these trade-offs in production LLM deployments.
Self-Guided Test-Time Training Improves Long-Context LLM Accuracy
A new arxiv paper proposes Self-Guided Test-Time Training (SG-TTT) to improve long-context utilization in LLMs without requiring labeled data. The method uses the model's own predictions to generate pseudo-labels for fine-tuning on the test context, addressing accuracy degradation in long inputs. This approach is more efficient than full TTT and shows promise for practical deployment.