AI

Google Gemma 3 explained: the open-weights small model family - sizes, context window, multimodality, architecture, benchmark scores, licensing and the VRAM and quantization options for on-device and edge deployment in 2026.

Multi-LoRA serving explained: how S-LoRA and Punica serve thousands of fine-tuned adapters on one base model - unified paging, heterogeneous batching, adapter routing and the multi-tenant cost trade-offs in 2026.

Claude Sonnet 5 explained: Anthropic's most agentic mid-tier model - the 1M-token context window, adaptive thinking, 82.1% SWE-bench Verified, $3/$15 pricing, and how it compares to Opus 4.8 and GPT-5.6 in 2026.

Expert-parallel MoE inference explained: how sparse MoE models are served with expert parallelism, all-to-all routing, load balancing, expert-cache and the latency/throughput trade-offs behind GLM, Kimi and DeepSeek in 2026.

FLUX image generation model explained: Black Forest Labs' rectified-flow transformer, the dev/pro/schnell weight tiers and licenses, GenEval and ELO benchmarks, VRAM to self-host and inference cost in 2026.

Inkling architecture explained: Thinking Machines Lab's 975B/41B-active open-weights multimodal MoE with 1M context, controllable thinking effort, Apache 2.0 weights, benchmarks and deployment cost in 2026.

Reasoning-effort control lets one model span cheap and deep modes: thinking-budget APIs, token accounting, routing policy, SLO impact and the failure modes of adaptive test-time compute in 2026.

Kimi K3 architecture explained: Moonshot's 2.8T open MoE with 16/896 experts, Kimi Delta Attention, 1M context, GPQA 93.5, Terminal-Bench 88.3, pricing and how it compares in 2026.

Prefill/decode disaggregation splits LLM inference into separate compute-bound and memory-bound pools: KV-cache transfer, SLO isolation, and when it beats co-located serving in 2026.

Continuous batching (in-flight batching) for LLM inference: iteration-level scheduling, prefill/decode interleaving, and how it lifts GPU throughput without hurting latency in 2026.