56 related articles

Local LLM crashing in Agent frameworks? The issue may be num_gpu set too high. Learn what num_gpu really controls (GPU layer offloading, not GPU count) and how to tune it for stable Agent performance.

Colibri uses MoE hot-cold separation and 4-bit quantization to run 744B-parameter models like GLM 5.2 on consumer hardware. Learn about its three-tier memory architecture and speculative decoding.

In-depth review of Poolside's Laguna S 2.1 open-source coding model: MoE architecture, RL training, DGX Spark local deployment, and real-world agentic coding tests with 8B active parameters.

Framework 13 Pro in-depth review covering build quality, display, performance, and Linux experience. This modular laptop achieves 85% of MacBook Pro's refinement with far superior repairability and upgradeability.

In-depth analysis of Apple Silicon local LLM inference speed benchmarks covering M-series memory bandwidth, model quantization, MLX framework optimization, and Mac configuration guidance.

Deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on RTX 5080. Exploring its architecture, Metal porting plans, and applications in VTuber, AR/VR, and sports analysis.

A deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on an RTX 5080.

Redis creator antirez built DS4 "Dwarf Star," a pure-C inference engine, using asymmetric quantization to compress DeepSeek V4 Flash from 500GB to 80.8GB for local 128GB unified-memory deployment at 26.7 tokens/sec.

Redis author antirez built the pure-C inference engine DS4 "Dwarf Star," compressing DeepSeek V4 Flash from 500GB to 80.8GB via asymmetric quantization for local deployment on 128GB unified memory at 26.7 tokens/sec.

Alibaba open-sources a 2.4 trillion parameter Qwen model and launches the Qwen Token Plan. Chinese models surge, Kimi K3 tops global rankings, and China's AI is reshaping the global competitive landscape.

Benchmarking 4×V100 16G PCIe vs. 2×V100 32G SXM adapter for local LLM inference. Prefill speed, decode speed, power limits, and bandwidth bottlenecks analyzed.

MiniMax M3 is a 428B MoE model. Its 23B active parameters reflect per-token compute, not VRAM needs. Learn the MoE trade-offs, quantization options, and deployment paths to avoid the most common misconception.
AirLLM: How a 4GB GPU Can Run a 70B Mo…
AirLLM is an open-source project that uses layer-by-layer inference to run 70B LLMs on a single 4GB GPU. Learn how it works, its tradeoffs, and ideal use cases.
Apple M7 Ultra Leaked: Can 1.5TB Unifi…
Apple's M7 Ultra chip rumored to feature 1.5TB unified memory targeting NVIDIA Blackwell-level AI performance. A deep dive into UMA advantages, what "matching Blackwell" really means, and Apple's AI strategy.

Complete guide to running local LLMs on a laptop with 8GB VRAM: real usable memory, quantization estimation, Q4 7B/8B model recommendations, Ollama setup, GPU offloading, and agent development tips.
Apple M7 Ultra Chip Leaked: Can 1.5TB …
Reddit leaks suggest Apple's M7 Ultra chip could feature up to 1.5TB unified memory. We analyze the architecture, pricing debate, bandwidth limits, and ecosystem trade-offs for local LLM inference.
Building an eGPU for Local LLM on a $1…
Building an eGPU for local LLMs on a $1,000 budget? This guide covers GPU selection, dock costs, RTX 3090 vs 3060 value, and top tools like Ollama and llama.cpp.
JAX Host Offloading: A Practical Guide…
Memory capacity is the core bottleneck in LLM training. This guide explores JAX-based host offloading — covering optimizer state offloading, activation strategies, PCIe bandwidth trade-offs, and how it complements activation recomputation.

A developer ports Tencent's Hunyuan3D to Apple MLX. M4 Max benchmarks: shape generation in 20s, 5.6GB peak RAM. Quantized versions run on iPhone. Modelr is the first local image-to-3D app for Apple Silicon.
How Fixing Three Bugs Made Qwen3.5-122…
A developer fixed three critical bugs to make Qwen3.5-122B run reliably as a daily driver on Mac Studio. Explore memory management, inference stability, and quantization precision.