AMD Ryzen AI Max+ Pro 400: Local LLM Inference on Workstations Explained
AMD Ryzen AI Max+ Pro 400 workstation chips target local LLM inference. Unified memory, NPU/iGPU split, model sizes that fit, and local vs cloud API economics.
ONNX Runtime 1.30 vs 1.29: What Changed for Edge AI in 2026
ONNX Runtime 1.30 adds Arm NEON/SVE LinearAttention, INT4 paged KV cache, Go bindings and FP16 fallback changes. What changed and how to upgrade safely.
vLLM 0.28 to 0.30 Migration: Model Runner V2 Default, Breaking Changes
Upgrading vLLM 0.28 to 0.30: Model Runner V2 is now default, MRV1 removal targeted for v0.32, new flags, removed env vars and a safe rollout checklist.
d-Matrix Corsair and the Rise of Dedicated AI Inference Silicon (2026 Analysis)
An analysis of d-Matrix Corsair and digital in-memory compute: why dedicated AI inference silicon is challenging GPUs on cost, latency, and energy for LLM serving in 2026.
Semantic Caching for LLM Applications: Architecture (2026)
A 2026 architecture guide to semantic caching for LLM apps: embedding similarity lookup, cache invalidation, hit-rate tuning, and where it quietly breaks.
FP8 vs INT8 vs INT4 LLM Quantization Benchmark (2026)
A 2026 LLM quantization benchmark comparing FP8, INT8, and INT4: accuracy retention, throughput, memory, and when each precision is the right call.
Mixture-of-Experts (MoE) LLM Architecture Explained (2026)
Mixture-of-Experts LLM architecture explained — routing, sparse activation, load balancing, expert parallelism, and the real serving trade-offs.
Edge AI Inference at Scale: NVIDIA Jetson, Intel, and Arm NPUs (Updated 2026)
Edge AI inference at scale, updated for 2026: NVIDIA Jetson Thor, Hailo and Arm Ethos NPUs, INT4/FP8 quantization, runtimes, and how to pick edge accelerators by TOPS-per-watt.