Inference & Cost
Free LLM APIs: What You Actually Get (Limits and Catches)
Free LLM APIs come with rate limits, token caps, and hidden catches. Learn what free tiers really offer and how to plan around their constraints.
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Guides
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Inference & Cost
Cheapest LLM APIs in 2026: Price Per Million Tokens
Compare the cheapest LLM APIs in 2026 by price per million tokens, and learn how tokenization and caching affect your real inference costs.
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Training & Fine-tuning
Gradient Checkpointing Explained: Trade Compute for Memory
Learn how gradient checkpointing cuts training memory by recomputing activations during backprop, and when the extra compute is worth it for large models.
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Training & Fine-tuning
How Much Data Do You Need to Fine-Tune an LLM?
Learn how many examples you really need to fine-tune an LLM with LoRA, from a few hundred to tens of thousands, and what factors change the answer.
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Training & Fine-tuning
Fine-Tune an LLM for Free on Google Colab
Learn how to fine-tune an LLM for free on Google Colab using LoRA and QLoRA, from setup to training and evaluation, step by step.
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Training & Fine-tuning
How Much GPU Memory to Fine-Tune Llama 3 8B
Estimate the GPU VRAM needed to fine-tune Llama 3 8B with LoRA: weights, gradients, optimizer states, activations, and practical batch-size tradeoffs.
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Inference & Cost
vLLM vs TGI vs Ollama for Production Inference
Compare vLLM, TGI, and Ollama for production LLM inference: throughput, KV-cache efficiency, deployment complexity, and when each engine fits.
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Inference & Cost
H100, A100, RTX 4090: GPU Cloud Rental Prices for LLM Hosting
Compare H100, A100 and RTX 4090 cloud rental prices for LLM hosting. Learn VRAM, throughput and cost-per-token tradeoffs to pick the right GPU.
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Inference & Cost
Is Self-Hosting an LLM Cheaper Than an API? Break-Even Math
Compare self-hosted LLM costs against API pricing. Learn the break-even math for GPU hardware, utilization, and tokens to pick the cheaper option.
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Inference & Cost
GGUF vs GPTQ vs AWQ: Which Quantization Format to Pick
Compare GGUF, GPTQ, and AWQ quantization formats on hardware fit, speed, and quality so you can choose the right one for local and served inference.
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Inference & Cost
Ollama vs LM Studio vs llama.cpp Compared
Compare Ollama, LM Studio and llama.cpp on setup, performance, control and use cases so you pick the right local LLM runtime for your workflow.
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Inference & Cost
Mac for Local LLMs: How Much Unified Memory Do You Need?
Learn how much unified memory a Mac needs to run local LLMs, from 7B to 70B models, with quantization tips and practical sizing guidance.
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