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.
LLM Semantic Router: An Inference Routing Pattern
The LLM semantic router pattern in 2026: route requests by intent and cost to the right model, with vLLM Semantic Router, embeddings, and a reference design.
LLM JSON Mode: A Structured-Output Benchmark (2026)
A 2026 benchmark of LLM JSON mode and constrained decoding: throughput, latency, and accuracy across grammar-based methods, with reproducible methodology.
LLM Prompt Caching: Architecture and Economics (2026)
How LLM prompt caching works in 2026: provider-side vs self-hosted KV reuse, cache-aware prompt design, hit-rate economics, and where it quietly breaks.
Does Edge AI Actually Cut Cloud Costs? A Fact-Check
Fact-checking the claim that edge AI slashes cloud bills: where the savings are real, where they hide capital and ops costs, and the break-even math for 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.