Lumino Learn
Practical GPU and AI infrastructure guides
Use these guides to plan VRAM, model serving, hosted APIs, GPU rental, and inference costs before your product traffic arrives.
GPT-5.6 developer guide
GPT-5.6 Explained: Sol vs Terra vs Luna for Developers
Compare GPT-5.6 Sol, Terra, and Luna across pricing, context, reasoning, agents, prompt caching, and production routing decisions.
DeepSeek V4 comparison
DeepSeek V4 Pro vs Flash: Which API Model Should You Use?
Compare DeepSeek V4 Pro and Flash for coding, agents, RAG, long context, prompt caching, latency, and completed-task cost.
Coding agent guide
Kimi K2.7 Code API: A Practical Guide for Coding Agents
A practical Kimi K2.7 Code guide for repository agents, tool loops, token efficiency, verification, and production guardrails.
GLM API guide
GLM 5.2 API Guide: Long Context, Coding Agents, Pricing and When to Use It
A practical GLM 5.2 API guide for developers comparing long-context coding agents, OpenAI-compatible access, cached-token pricing, local serving, and GPU rental tradeoffs.
LLM infrastructure guide
VRAM Calculation for LLMs: Weights, KV Cache, Context and Batch Size
A practical guide to estimate GPU VRAM for LLM inference before renting hardware, including model weights, quantization, KV cache, context length, and runtime overhead.
Long-context inference
Long Context Is a GPU Memory Problem Before It Is a Prompting Feature
Why long-context AI apps hit GPU memory, TTFT, and throughput limits, and how to plan KV cache, batching, and fallback APIs before users arrive.
Inference cost control
LLM Inference Cost Leaks: The GPU Is Not Always the Expensive Part
A practical guide to the hidden cost leaks in LLM inference: idle GPUs, long outputs, retries, cold starts, oversized context, and unmanaged routing.