💻 Local LLMs

Model Quantization, Inference Optimization, GGUF Format, Privacy-preserving AI

Unlocking LLMs: Secure Inference for the Rest of Us
dev.to·16h·
Discuss: DEV
🤐Secure Multiparty
LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation
arxiv.org·12h
⚡LZ4 Streaming
Lockdown LLMs: Unleashing AI Power While Safeguarding User Privacy
dev.to·8h·
Discuss: DEV
🤐Secure Multiparty
I Ran Local LLMs on My Android Phone
itsfoss.com·4h
🔌Offline-first Apps
Unlocking LLMs: Secure, Efficient Inference for Everyone
dev.to·2d·
Discuss: DEV
🔒Privacy Archives
Python, Deep Learning, and LLMs: A Crash Course for Complete Beginners
python2llms.org·17h·
Discuss: Hacker News
🎵Audio ML
Private LLM Inference: Democratizing AI with Ciphertext Computations
dev.to·1d·
Discuss: DEV
🤐Secure Multiparty
AI Unleashed: Secure LLM Inference for Everyone
dev.to·1d·
Discuss: DEV
🤐Secure Multiparty
Defeating Nondeterminism in LLM Inference – Thinking Machines Lab
jmason.ie·4d
✓Automated Theorem Proving
A key type of AI training data is running out. Googlers have a bold new idea to fix that.
businessinsider.com·59m
🔍Vector Forensics
I built an LLM from Scratch in Rust (Just ndarray and rand)
github.com·1d·
Discuss: Hacker News, r/LLM, r/rust
🦀Rust Macros
LLMs on a Shoestring: The Dynamic Cache Advantage by Arvind Sundararajan
dev.to·7h·
Discuss: DEV
💨Cache Optimization
AI for Everyone: Secure Language Models Without the Hardware Hype
dev.to·1d·
Discuss: DEV
🤐Secure Multiparty
Unlocking LLM Power: Secure and Cost-Effective Inference for Everyone by Arvind Sundararajan
dev.to·1d·
Discuss: DEV
🤐Secure Multiparty
VaultGemma: The world's most capable differentially private LLM
research.google·3d·
Discuss: Hacker News, r/LocalLLaMA
🛡️Differential Privacy
How to Train an LLM-Recommender Hybrid that Speaks English & Item IDs
eugeneyan.com·1d
🔍Information Retrieval
Disaggregated Inference at Scale with PyTorch and VLLM
pytorch.org·1d·
Discuss: Hacker News
⚡LZ4 Streaming
Demystifying LLM Tuning: XAI-Powered Optimization Unveiled by Arvind Sundararajan
dev.to·15h·
Discuss: DEV
🏭Compiler Backends
The Data Backbone of LLM Systems
infoq.com·4d·
Discuss: Lobsters
🔗Constraint Handling
The Great Debate: Open-Source LLMs vs Proprietary Models
dev.to·11h·
Discuss: DEV
🔓Open Source Software