81 — Dynamic Model Routing
Select an eligible model for each task using measured capability, quality, latency, and cost while keeping policy constraints mandatory.
Model Optimization, Deployment & Infrastructure
Balance quality, latency, capacity, and cost. Turn model capabilities into dependable services.
How does the system behave at scale?
- Measure quality before optimizing serving cost.
- Test tail latency with representative traffic.
- Version models, prompts, and evaluation evidence together.
82 — Small Language Models (SLMs) Deployment
Deploy compact models for bounded tasks where local control, latency, or operating cost matters and measured capability is sufficient.
83 — Speculative Decoding
Use a cheap proposal mechanism and target-model verification to reduce decoding latency only where acceptance and workload conditions justify the overhead.
84 — Model Quantization (AWQ/GGUF)
Reduce model representation cost using hardware-compatible quantization while validating task quality, memory headroom, and actual serving speed.
85 — LLM Cold Start Optimization
Shorten the path from a scheduled inference replica to usable capacity by measuring and optimizing each initialization stage.
86 — Streaming LLM Responses
Deliver incremental model output with explicit completion, cancellation, validation, and reconnect semantics rather than treating chunks as a finished answer.
87 — Batch Prompting
Amortize work across independent items while preserving item identity, isolation, validation, and retry boundaries.
88 — vLLM / TensorRT-LLM Engines
Treat the inference engine as a scheduled memory-and-compute system whose configuration must match models, hardware, and service objectives.
89 — Serverless LLM Architecture
Separate request admission from elastic inference capacity so serverless execution can scale safely despite model startup and duration constraints.
90 — LoRA / QLoRA Fine-Tuning
Adapt a base model through low-rank trainable updates, optionally over a quantized frozen base, with explicit compatibility and evaluation controls.
91 — GPU Cluster Orchestration (Kubernetes/KServe)
Coordinate accelerator placement, model readiness, autoscaling, and recovery as one serving system rather than treating GPUs as ordinary interchangeable CPU slots.
92 — Pre-Computing Embeddings
Compute reusable document vectors ahead of requests using content identity, version lineage, and an explicit freshness strategy.
93 — Model Versioning and A/B Testing
Version the complete inference behavior and compare releases through stable assignment, meaningful outcome measures, and reversible promotion.
94 — Token Cost Allocation Tracking
Attribute inference usage to the business work that caused it, including retries, caches, tools, and shared infrastructure.
95 — Distributed Memory Architectures
Distribute model weights, activations, and attention state across devices with an explicit communication and failure model.
96 — Semantic Similarity Analytics
Analyze semantic neighborhoods to find retrieval gaps, repeated intents, and content drift without treating vector distance as an objective truth score.
97 — Hardware-Accelerated Guardrails
Accelerate selected safety and policy checks while keeping their detection limits, ordering, and failure behavior explicit.
98 — Local Model Ingress/Egress
Control how requests enter a locally hosted model and how data, tools, telemetry, and updates leave its environment.
99 — Context Caching (Anthropic/OpenAI Feature)
Reuse provider-supported computation for stable prompt prefixes while preserving fresh generation and provider-specific eligibility, retention, and billing rules.
100 — Asynchronous Fine-Tuning Pipelines
Run data preparation, adaptation, evaluation, and model promotion as a durable offline workflow with explicit quality gates and lineage.