Developer Claims 9.9x Lower TTFT on Android by Reusing llama.cpp KV State
A developer has reported achieving a 9.9x reduction in time-to-first-token (TTFT) for local large language model inference on a real Android device. The improvement was achieved by reusing KV cache…
Read the full articleYou might also wanna read
LMCache: A KV Cache Management Layer for Scalable LLM Inference
Learn how LMCache reduces TTFT and improves throughput for LLM inference with tiered KV cache offloading, non-prefix reuse, PD disaggregatio
ntransformer: C++/CUDA LLM Inference Engine Enables Running Llama 70B on RTX 3090
High-efficiency LLM inference engine in C++/CUDA. Run Llama 70B on RTX 3090. - xaskasdf/ntransformer

Research-Driven Coding Agents Improve llama.cpp Performance with Literature Search Phase
Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends —

I ran 133 benchmarks to find out if vLLM is actually faster than HuggingFace
Spoiler: it depends on what “faster” means to you If you’ve ever tried to serve a large language model in production, you’ve probably come a

Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
arXiv:2607.08057v1 Announce Type: cross Abstract: Despite the rapid advancements of large language models (LLMs), LLM serving systems remain
Reame: A CPU-first LLM inference server built on llama.cpp for existing low-cost hardware
Reame — CPU-first LLM inference server on llama.cpp: disk KV cache, self-regulating speculation, generation archive, interleaved multi-user,
Reame: A CPU-first LLM inference server built on llama.cpp for existing low-cost hardware
Reame — CPU-first LLM inference server on llama.cpp: disk KV cache, self-regulating speculation, generation archive, interleaved multi-user,

Comments
Sign in to join the conversation.
No comments yet. Be the first.