관심도 점수
100
- 검색 slug
- llama-cpp
- 실제 연결
- Hacker News, GitHub, arXiv
- 키워드
- and, the, llama.cpp, inference, model, llm, local, with
- 마지막 수집
- 2026-09-21T08:57:15.200Z
출처별 탭
The Illusion of Local Privacy: Confidentiality Boundary Failures in Consumer LLM Serving Systems
Running large language models (LLMs) locally is often considered more private than cloud-hosted inference because user prompts remain on the device. We ask whether keeping inference local is, by itself, sufficient to keep those prompts confidential. Our results show that it is not: prompt confidentiality also depends on how the surrounding serving software handles prompt data before, during, and after inference. We examine four boundaries at which prompt confidentiality can fail in consumer local-LLM serving syste...
GGUF-Metadata Prediction of Single-Sequence llama.cpp Throughput Across Three Systems
We predict single-sequence model throughput from GGUF metadata using roofline-shaped predictors with quantization-specific scale factors fitted on reference models. The scored cohort comprises 318 phase-depth measurements from 53 host-file configurations on two Apple M4 Max systems and an NVIDIA RTX 5080. On host-specific held-out sets of four, five, and two configurations, an active-parameter decode model obtains 13.1%, 14.4%, and 36.1% mean absolute percentage error (MAPE), versus 49.4%, 55.3%, and 51.9% when ch...
A Voice-Interactive Multi-Agent System for Smart Operating Rooms: Architecture Design and Key Technologies
This paper presents SurgicalRoomAgent, a voice-interactive multi-agent system for smart operating rooms based on large language models (LLMs). The system achieves natural language understanding, device control, intraoperative recording, and surgical report generation through a layered architecture comprising a voice interaction pipeline (wake, ASR, turn detection, agent reasoning, TTS) and an agent core (skill registry, task planner, device manager). Three key technologies are investigated: (1) KV Cache prefix war...
mzCache: On-Device LLM Memory Management under Multitasking
On-device mobile Large Language Model (LLM) inference is gaining significant attention. However, mobile devices operate in highly dynamic multitasking environments where users frequently switch between applications. This creates memory pressure, forcing LLM memory (model weights and KV cache) to be evicted by the operating system. When a new inference request arrives, the inference system must restore the evicted memory through slow storage reads or recompute the entire KV cache, severely degrading responsiveness....
Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform, memory traffic, and power management all affect latency and efficiency. We present Hydra, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs. Hydra instruments HuggingFace Transformers and llama.cpp with a shared per-prompt timing schema and fuses those records with hardware telemetry, enabling a multi-dimensional characterization of performance, system-resource ut...
Pipeline-Native Transformers: Co-Designing Model Architecture and CPU Inference for Bandwidth-Efficient Autoregressive Decode
Single-token autoregressive decode on CPUs is bound by memory bandwidth, not arithmetic: a modern CPU sustains roughly 1 TFLOP/s of compute but only about 50 GB/s from main memory, and each generated token must stream every active weight once. This report argues that the most effective response is to co-design the model architecture and the inference runtime together. It presents cflow, a CPU-first streaming engine, alongside a family of pipeline-native transformer architectures whose inter-layer dependency graphs...
Don't Repeat Yourself: Stopping Verbatim Loops at Sampling Time
Large Language Models generate text autoregressively, but open-ended generation is prone to verbatim looping, in which models repeat spans already present in context. Standard defenses such as repetition, presence, and frequency penalties and n-gram blocking act on token recurrence rather than the sequential structure of a loop, and often suppress looping only at strengths that also degrade formatting or fluency. We propose Don't Repeat Yourself (DRY), a sampling-time logit adjustment that penalizes a candidate to...
XTC: Head-Aware Sampling by Excluding Top Choices
Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-probability tail. These strategies overlook a common regime of open-ended generation in which several continuations are plausible but too much probability mass remains concentrated on the most generic choice. We introduce XTC (Exclude Top Choices), a lightweight head-aware decoding operator that targets this regime directly. XTC identifies tokens whose probabilities excee...
NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching
Deploying large language models on edge devices is increasingly limited by a widening gap between model size and available memory. Existing approaches such as quantization, smaller models, and offloading can raise the effective memory limit, but they still assume that the model can be compressed or partitioned to fit within some budget. We target the harder model-exceeds-memory setting, in which the model remains larger than resident memory throughout execution and storage becomes an active source of weights on th...
Open at the Edge, Captured at the Center: llama.cpp and the Political Economy of Local AI Inference
Critical scholarship on open AI has focused on model releases and cloud ecosystems, leaving the local inference infrastructure that makes open-weight models runnable on user-owned devices largely unexamined. We address this gap through a mixed-methods analysis of llama$.$cpp, combining 7,681 merged pull requests from March 2023 through March 2026 with repository discussions, corporate statements, and contributor blogs. We show that local inference broadens participation at execution while relocating capture into t...
Michael-A-Kuykendall/shimmy
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
ngxson/smolvlm-realtime-webcam
Real-time webcam demo with SmolVLM and llama.cpp server
LearningCircuit/local-deep-research
~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
mostlygeek/llama-swap
Reliable model swapping for any local OpenAI/Anthropic compatible server - llama.cpp, vllm, etc
h2oai/h2ogpt
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/
abetlen/llama-cpp-python
Python bindings for llama.cpp
serge-chat/serge
A web interface for chatting with Alpaca through llama.cpp. Fully dockerized, with an easy to use API.
ggml-org/llama.cpp
LLM inference in C/C++
PawanOsman/OpenCursor
Open-source Cursor-like AI coding agent for VS Code - agentic chat, multi-provider LLMs (OpenAI, Ollama, llama.cpp), semantic search, and MCP support
intel/ipex-llm
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.