13 min read/0 views
vLLM vs TGI vs Ollama: Self-Hosting LLMs Without Burning Money or Losing Sleep
Ollama peaks at 41 tok/s. vLLM hits 793. TGI is in maintenance mode. Here's the self-hosting guide I wish existed before I started.
Tag
46 articles
Ollama peaks at 41 tok/s. vLLM hits 793. TGI is in maintenance mode. Here's the self-hosting guide I wish existed before I started.
I spent 6 months parsing LLM output with regex. Then Pydantic + structured outputs eliminated every 3 AM parsing alert. Here's the migration.
Our LLM bill hit $23K/month. Three layers — prompt caching, semantic caching, and model routing — cut it to $8.6K. Here's how.
65% of companies use generative AI. Almost none test it properly. Here's the eval framework that caught our $47K hallucination disaster.
88% of AI agents never reach production. $547B in failed AI investments. The five gaps that kill agents and the architecture that actually survives.
OpenAI at $852B. Anthropic at $380B. Databricks at $134B. Over $1.3T in private valuations heading for public markets. Bubble or boom?
Sora cost $15M/day to run. Lifetime revenue: $2.1M. Context windows keep growing. The economics that decide which AI products survive.
SWE postings down 49% from peak. AI roles up 340%. Junior hiring collapsed 73%. The market is bifurcating and depth sets the price.
A $47K recursive loop went undetected for 11 days. MLOps can't monitor agents. The new operational stack for autonomous AI is emerging fast.
A rigorous RCT found AI coding tools slowed down experienced developers by 19%. The developers themselves believed they were 20% faster. The perception-reality gap changes everything.
Karpathy coined both terms a year apart. One builds $400M startups. The other lost Amazon 6.3 million orders. The difference is about to define which developers thrive.
Meta shipped 10M-token context. The model scores 15.6% at 128K tokens. Here's what actually works and what doesn't.
Every major open-source frontier model in 2026 uses MoE. A 120B model now fits on one H100. The self-hosting economics changed forever.
Alibaba's Qwen hit 1B+ downloads, beats GPT-5.2 on instruction following, and costs 13x less than Claude. The open-source AI race is over.
Microsoft launched MAI models built by 10-person teams that beat OpenAI's Whisper. The $13B partnership is fraying.
All three score ~57 on the Intelligence Index. Claude leads coding quality, Gemini leads math, GPT leads speed. Which to use when.
Sora burned $15M/day in compute against $2.1M lifetime revenue. The most expensive lesson in AI product economics.
LangChain chains steps in a line. LangGraph builds state machines. Most comparisons miss this fundamental difference.
Rakuten launched 'Japan's largest AI model' with government backing. It was a fine-tuned DeepSeek V3 with the MIT license deleted. The community caught it in four hours.
$1 trillion wiped from SaaS stocks in Q1 2026. AI agents are shrinking seat counts. But the real threat is pricing, not existence.
A realistic month-by-month roadmap with salary data, skill requirements, and what most guides get wrong.
The EU AI Act's high-risk obligations hit in August 2026. Only 14% of companies are prepared. Here's what developers building with AI need to know — risk tiers, technical requirements, GPAI rules, and a practical compliance checklist.
MCP went from Anthropic side project to industry standard in 16 months. Here is how it works and why it matters.
Build a RAG chatbot with LangChain, OpenAI embeddings, and Neon PostgreSQL. pgvector, no Pinecone, full Python code, 30 minutes.
Data centers consumed 415 TWh in 2024 — more than the UK. The IEA projects 945 TWh by 2030. Big Tech emissions are rising 23-60% despite net-zero pledges. Here's what's actually happening.
Benchmarks measure what model creators optimize for, not what matters in production. Here is what I measure instead.
24,000+ fake accounts. 16M+ exchanges. DeepSeek, MiniMax, Moonshot accused of industrial-scale model theft. The ethics, the hypocrisy, and the national security framing.
Apple spends $14B on AI while competitors spend $650B. Is it losing or playing a smarter game? The data tells a complicated story.
OpenAI acquired Astral, the company behind uv, ruff, and ty. What it means for Python's most loved tools.
AI Engineer topped LinkedIn's fastest-growing jobs list, yet most companies can't agree on what the role actually means.
Agentic AI and reinforcement learning are different things. The confusion costs companies wrong hires, wrong architecture, and wrong expectations.
The market says $200B by 2034. The data says 95% of agent projects fail before production. Here is what actually works.
I tested Claude Code, GitHub Copilot, and Cursor daily for months. Here's which wins for each task.
When Graph RAG doubles retrieval accuracy and when it wastes your money. Benchmarks, costs, frameworks, and a decision framework.
A2A lets AI agents discover, delegate, and coordinate without knowing each other's internals. Here is how it works.
They sound similar but the day-to-day, salary ceiling, and career trajectory are completely different. Here is how to choose.
AI automated 30-40% of the old analyst job. The remaining 60% pays better than ever. Here is what the role actually looks like now.
A phase-by-phase roadmap to become an AI engineer: LLMs, RAG, agents, and what interviews actually ask.
Razer RTX 5090, MacBook M4 Max 128GB, ThinkPad P16, Framework 16, and a $1,300 budget pick. Compared.
Graph databases find connections. Vector databases find similarities. When to use which, real benchmarks, and why PostgreSQL might replace both.
In 2005, "software engineer" meant one thing. In 2026, there are 20+ titles. Which splits are real and which are hype?
RAG tutorials teach the easy 20%. Here are the five production problems they skip — and how to actually solve them.
I replaced GPT-4 with 7B models in production. Same quality, 95% cheaper. Here is why small language models are winning.
Most teams don't need Pinecone. pgvector benchmarks, decision framework, and when dedicated vector DBs actually make sense.
Prompt engineering jobs are vanishing. Context engineering, harness engineering, and agentic AI are what actually matter now.
A practical guide to fine-tuning LLMs with LoRA, QLoRA, Unsloth, and OpenAI. Real costs, real code, and when to fine-tune vs RAG.