LLMOps

Answer-first: For 80% of domain enterprise tasks, fine-tuned Small Language Models (1B–14B) match frontier performance at 1/50th inference cost and sub-40ms latency. This playbook provides the production engineering blueprint: synthetic data curation, QLoRA fine-tuning with Axolotl on 24GB GPUs, DeepSeek-R1 reasoning distillation, DPO/GRPO alignment, and vLLM continuous batching deployment. 🎯 Series Overview: Why Small Language Models in 2026? Relying exclusively on proprietary frontier API models (GPT-4.5, Claude 3.5 Sonnet) introduces three fatal enterprise vulnerabilities: ...

← Previous Chapter: Executive Summary | Series Hub | Next Chapter: Part 2: SFT Data Engineering → Prerequisite: Read Executive Summary: The Rise of Specialized Small Language Models for cost break-even formulas and hybrid AI architectural framing. Answer-first: The Hybrid AI Routing architecture evaluates incoming request complexity and token uncertainty in under 3.5ms. 80% of structured queries are served locally by fine-tuned 7B models on vLLM within 35ms TTFT, while low-confidence requests automatically cascade to Claude 3.5 Sonnet through a localized PII sanitization proxy, cutting monthly API bills by 85%. ...

Tech Radar: vLLM v1 Production Engine Architecture & Distributed KV Cache Optimization: PagedAttention v3, Dynamic Chunked Prefill & RoCEv2 Zero-Copy Transfers Answer-First: vLLM v1 re-engineers production LLM serving by replacing Python-Ray actor coordination with a zero-overhead C++ core and lock-free execution loop. Coupling PagedAttention v3, dynamic chunked prefill, and multi-tier RoCEv2 KV offloading slashes P99 TTFT by 78% (410ms to 92ms), restricts memory fragmentation to <2.4%, and boosts 8x NVIDIA H100/H200 cluster throughput by 2.7x. ...

Tech Radar: Disaggregated Prefill-Decode Architecture: Decoupling Compute & Memory Bandwidth via RoCEv2 KV-Transfer Answer-First: Disaggregated Prefill-Decode serving establishes the 2026 enterprise LLM standard by physically decoupling compute-dense prefill nodes from memory-bandwidth-bound decode clusters. Streaming active KV caches over 400Gbps RoCEv2 fabrics with kernel-bypass RDMA slashes P99 TTFT by 11x (420ms down to 38ms), eliminates inter-request latency jitter, and scales cluster throughput 2.8x on 64x NVIDIA H100 deployments. Prerequisite: Readers should possess solid foundations in distributed LLM serving architectures (continuous batching, PagedAttention, Tensor Parallelism), high-performance network protocols (RoCEv2, InfiniBand RDMA, GPUDirect RDMA zero-copy), and cluster orchestration across heterogeneous GPU nodes (NVIDIA HGX H100/H200). ...

Tech Radar: SGLang EAGLE-2: Speculative Decoding & Tree-Attention Latency Acceleration Answer-First: SGLang’s native EAGLE-2 implementation establishes the 2026 state-of-the-art for autoregressive latency acceleration, overcoming the memory-bandwidth wall (<1 FLOP/byte) by combining lightweight multi-layer feature extrapolation with dynamic tree-attention verification. On 8x NVIDIA H100 GPU clusters, EAGLE-2 achieves an empirical 2.5x to 3.5x generation speedup and reduces Time-Per-Output-Token from 35.7ms to 11.6ms on Llama-3-70B, with mathematically zero output distribution degradation. Prerequisite: Readers should understand Transformer autoregressive decoding mechanics (arithmetic intensity under single-token generation, KV cache memory footprint), basic speculative decoding principles (draft-and-verify loops), and GPU runtime profiling (CUDA event queries, Tensor Core saturation, and PyTorch/Triton kernel execution). ...

Tech Radar: DeepSeek-V3 Multi-Head Latent Attention (MLA) Architecture & KV Cache Compression Answer-First: DeepSeek-V3’s Multi-Head Latent Attention (MLA) overcomes inference memory bandwidth bottlenecks by projecting Keys and Values into a low-rank latent compressed space (d_c = 512). MLA achieves a 75% VRAM reduction versus MHA/GQA while preserving full attention expressive capacity via Decoupled Rotary Position Embedding (RoPE), enabling 4x larger batch sizes on standard GPU clusters. Prerequisite: Readers should possess a rigorous understanding of multi-head self-attention mathematics (query-key-value projections, attention score matrices, softmax scaling), Rotary Position Embedding (RoPE) mechanisms, GPU memory bandwidth limits during autoregressive decoding, and distributed MoE architectures. ...

Tech Radar: vLLM Context-Aware Routing & MLA KV Cache Architecture Answer-first: Multi Head Latent Attention combined with Context Aware Prefix Routing in vLLM eliminates the GPU memory bandwidth wall during multi turn autonomous agent execution loops. Compressing Key and Value states into 512 dimensional latent vectors and routing shared prefix requests via bounded load consistent hashing reduces VRAM footprint by 75% and cuts TTFT to 165ms. Prerequisite: Readers should be familiar with Transformer self-attention matrix mechanics, Rotary Position Embeddings (RoPE), GPU High-Bandwidth Memory (HBM) memory hierarchies, vLLM PagedAttention block allocation, and Layer-7 consistent-hash routing in Kubernetes. ...