Applied Workloads
Sprint priority 2. Building agentic automations and applied AI workloads that run on Lyceum: agents behind the OpenAI-compatible API, serverless execution with scale to zero, batch fan-out, document pipelines, image and video generation. EU-hosted agents for GDPR contexts.
16 articles
Subclusters
Articles
5 October 2026
Multi-Step Agent Workloads: Latency and Cost Budgets
An agent's latency is the sum of its sequential steps and its cost the sum of its calls, and neither is visible from a single request. This guide sets a task-level latency and cost budget, allocates it across step types, and adds a termination rule before the agent is built.
28 September 2026
RAG Pipeline GPU Sizing: Cost Per Query, End to End
Estimating the cost of a RAG pipeline requires decoupling the compute economics of embedding, vector search, and LLM generation. Transitioning from retail API markups to dedicated GPU infrastructure fundamentally lowers the cost per query when scaled for batch concurrency.
14 September 2026
AI Video Agents: Real-Time Generative GPU Infrastructure
This guide helps teams test latency, concurrency and cost before building an interactive video product. Measure your exact model and hardware, including the scheduling and sharing options your latency budget permits.
9 September 2026
Running Wan 2.2 on Cloud GPU: VRAM Needs & Cost per Clip
Lyceum does not provide a serverless API for Wan 2.2. Instead, deploying open-weight video models requires On-demand GPU VMs, where cost per clip is driven by VRAM scaling, hardware matching, and utilization.
16 September 2026
Self-Hosted TTS on GPU vs API: The Voice-Synthesis Cost Cliff
Moving text-to-speech off an API and onto a GPU replaces a linear per-character bill with a flat hourly rate, creating a clear cost crossover. This guide provides the exact break-even arithmetic to determine when self-hosting voice models becomes cheaper than paying a vendor.
15 September 2026
What It Costs to Train a Text-to-Video Model: Real GPU Budgets
Calculate the real cost of a text-to-video training run using active parameters, latent tokens, and dense MFU. Build a realistic campaign budget in GPU-hours to multiply by your own quoted hardware rates.
8 September 2026
AI Dubbing Costs: GPU Pipelines vs Commercial APIs
If you are costing an AI dubbing feature, this guide breaks the chain into its four stages and prices each one built against bought. Compare a self-built GPU pipeline against commercial dubbing APIs on a normalised cost per minute of finished audio.
7 September 2026
GPU Cost for Batch Vision Inference: DINOv3, SAM & CLIP
If a batch vision job is running slower than the GPU suggests it should, this guide finds the real bottleneck first and then sizes batch, resolution and precision around it.
7 September 2026
Whisper Transcription: GPU Cost & Batch Throughput Sizing
Running batch speech-to-text on massive audio archives through managed APIs scales costs linearly with every audio hour you send. Moving Whisper pipelines to self-hosted European GPUs and optimizing with CTranslate2 converts that per-minute bill into a GPU-hour bill you can size, measure and control.
7 June 2026
GPU Vector Database Cloud Integration: Architecture Guide
Vector databases are hitting the billion-vector scale, and CPU-bound indexing is choking under the load. Moving vector search to GPUs cuts index build times by up to 17x, but deploying this infrastructure requires strict attention to data sovereignty and cost control.
6 June 2026
Tool Calling Latency in LLM Inference: Production Optimization
Tool calling transforms language models into capable agents, but it introduces massive latency bottlenecks. Learn how to optimize inference engines, reduce token overhead, and deploy high-performance infrastructure.
5 June 2026
Scaling Multi-Agent Orchestration: GPU Memory, Inference, and Costs
Multi-agent systems work flawlessly on a local machine but break under production load. Learn how to decouple orchestration from inference and scale your GPU infrastructure efficiently.
5 June 2026
RAG Pipeline GPU Infrastructure: The Engineering Guide
You built a RAG pipeline. It retrieves 20 chunks, sends 32,000 tokens to the LLM, and your GPU throws an Out of Memory (OOM) error. Memory management in RAG is not a software problem. It is a hardware budget.
4 June 2026
The 2026 Guide to GPU Infrastructure for AI Agents
Autonomous AI agents demand distributed infrastructure optimized for latency and bursty traffic. Building for agentic workflows requires rethinking VRAM allocation, cold starts, and compliance.
3 June 2026
EU Compliant AI Agent Infrastructure: The 2026 Engineering Guide
Agentic AI multiplies token consumption compared to standard generative AI, because every reasoning step resends the accumulated context. Running these workloads on non-sovereign infrastructure exposes engineering teams to compliance risks and unsustainable hyperscaler costs.
30 May 2026
Deploy Whisper Large v3 GPU API: VRAM, Performance & EU Hosting
Running Whisper Large v3 in production requires strict VRAM management and optimized inference engines. For European teams, it also demands provable data sovereignty.