AI Compute 101
Foundational guides to understanding GPU compute for LLM inference. From VRAM basics to multi-GPU scaling — everything you need to deploy models in production.
What is VRAM and How Much Do LLMs Actually Need?
VRAM is the single most important spec for running LLMs locally. This guide explains exactly how much VRAM you need for every model size, from 7B to 700B+.
Read Guide →LLM Quantization Explained: FP16 vs FP8 vs INT4/AWQ
Quantization cuts VRAM usage by 2-8x with minimal quality loss. Understand FP16, FP8, INT8, and INT4/AWQ precision formats and when to use each.
Read Guide →What is KV Cache? Memory Scaling in Long-Context Inference
KV-cache is why LLMs need so much more VRAM than their weight size suggests. Learn how it scales with context length and how to optimize it.
Read Guide →Tensor Parallelism vs Pipeline Parallelism: Multi-GPU Scaling
When one GPU isn't enough, you need multi-GPU parallelism. Understand TP vs PP, when to use each, and how NVLink/InfiniBand affect scaling efficiency.
Read Guide →Top Free LLM APIs in 2026: Rate Limits, Keys & Verified Endpoints
Compare the best free LLM APIs in 2026 — Groq, Google AI Studio, Cerebras, Kilo Gateway, and more. Verified rate limits (RPM/RPD), zero-card requirements, and drop-in OpenAI SDK integration.
Read Guide →Top LLM Companies & Model Providers (2026 Technical Guide)
Compare the top 13 LLM companies: frontier labs, open-weights champions, and high-speed inference clouds. Side-by-side pricing, licensing trade-offs, and strategic verdicts for prototyping vs production.
Read Guide →Top 5 Image Generation Models & GPU Sizing Guide (2026)
Complete hardware sizing guide for FLUX.1, SD 3.5, and Midjourney alternatives. Compare FP16 vs FP8 VRAM requirements, ComfyUI setups, and cheapest cloud GPUs.
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