LLM inference - IoT Digital Twin PLM

Diffusion LLMs: How Text Diffusion Models Work (2026)

By MPRAUTO MPRAUTO July 25, 2026AINo Comments

Diffusion LLMs explained: how text diffusion models replace next-token prediction with iterative denoising and parallel decoding - masked discrete diffusion, remasking, throughput trade-offs vs autoregressive models in 2026.

Constrained Decoding: Architecture for Guaranteed-Valid LLM Output (2026)

By MPRAUTO MPRAUTO July 8, 2026AINo Comments

How constrained decoding guarantees valid LLM output: grammars, FSAs, token masking, JSON-schema enforcement, and where structured generation breaks in production.

On-Device SLM Inference: A 2026 Edge GPU Benchmark

By MPRAUTO MPRAUTO June 6, 2026AINo Comments

A 2026 benchmark methodology for small language models on edge GPUs — latency, tokens/sec, memory, and cost for Phi, Gemma, and Qwen on Jetson-class hardware.

vLLM Cost Economics: 2026 Deep Dive on $/Million Tokens

By MPRAUTO MPRAUTO June 3, 2026AINo Comments

A practical 2026 deep dive on vLLM cost economics — KV cache, paged attention, speculative decoding, and dollar-per-million-tokens math.

SGLang vs vLLM vs TensorRT-LLM: 2026 Inference Benchmark

By MPRAUTO MPRAUTO June 2, 2026AINo Comments

Reproducible 2026 benchmark of SGLang, vLLM, and TensorRT-LLM — throughput, p50/p99, KV cache utilization, and when each wins.

KV Cache Optimization for LLM Inference: A Deep Dive

By MPRAUTO MPRAUTO May 25, 2026AINo Comments

KV cache optimization for LLM inference — PagedAttention, quantization, prefix caching, and eviction, with the memory math behind each technique.

Q2 2026 LLM Inference Benchmark: vLLM vs TGI vs SGLang vs Triton

By MPRAUTO MPRAUTO April 29, 2026AINo Comments

Q2 2026 LLM inference benchmark across vLLM, TGI, SGLang, and Triton — throughput, p50/p99 TTFT/TPOT, KV-cache efficiency, and which engine wins per workload class.

OpenAI o3 Reasoning Models: Test-Time Compute Scaling Explained

By MPRAUTO MPRAUTO April 23, 2026AINo Comments

How OpenAI's o3 family scales reasoning at inference time — chain-of-thought RL, verifier models, cost curves, and when test-time compute beats pre-training.