GPU & LLM Compute Guides
In-depth guides on GPU cloud pricing, LLM API costs, and how to choose the right compute for your AI workload. Written by the Compute Comparison team.
H100 vs A100: Which GPU Should You Use for LLM Training in 2026?
A deep-dive comparison of NVIDIA H100 and A100 for large language model training — performance, cost-per-FLOP, availability, and when each makes sense.
The Cheapest GPU Cloud Providers for AI Inference in 2026
A ranked comparison of the most cost-effective GPU cloud providers for production AI inference — including spot pricing, per-second billing, and hidden costs.
How to Calculate LLM API Costs: A Complete Guide for 2026
Everything you need to know about LLM API pricing — tokens, context windows, prompt caching, batch discounts, and how to estimate costs for your application.
GPU Spot vs On-Demand Pricing: When to Use Each (2026)
A practical guide to GPU spot and on-demand pricing — interruption rates, cost savings, workload suitability, and provider-by-provider spot availability.
GPU Benchmark Guide for Machine Learning: What the Numbers Actually Mean
A practical breakdown of GPU benchmarks for ML workloads — FLOPS, memory bandwidth, NVLink, and how spec-sheet numbers translate to real training and inference throughput.
LLM Context Window Costs: How Long Contexts Affect Your API Bill
How context window length drives LLM API costs — with worked examples for RAG, chat history, and document processing workloads across GPT-4o, Claude, and Gemini.
Real-Time AI Inference Cost Guide: Self-Hosted vs Managed API (2026)
Cost breakdown and ROI analysis for real-time AI inference — self-hosted GPU vs managed LLM API, latency budgets, autoscaling strategies, and cost-per-request benchmarks.
AI Image Generation Costs in 2026: Stable Diffusion vs Managed APIs
Full cost breakdown for AI image generation — self-hosted Stable Diffusion vs Replicate, Stability AI, and DALL-E 3, with cost-per-image benchmarks and break-even analysis.
RAG Pipeline Cost Guide: Embeddings, Vector DB, and LLM Inference (2026)
End-to-end cost breakdown for production RAG pipelines — embedding models, vector database hosting, retrieval compute, and LLM inference, with benchmarks from 10K to 10M daily queries.
LLM API vs Self-Hosted: The Definitive Cost Comparison for 2026
Break-even analysis by model tier, cost-per-token benchmarks, hidden costs on both sides, and a decision framework from prototype to enterprise scale.
Fine-Tuning Llama Cost Guide: 8B, 70B, and 405B (2026)
Complete cost breakdown for fine-tuning Llama 3.1 — GPU memory requirements, training time and cost by technique (full fine-tune vs LoRA vs QLoRA), managed APIs, and ROI analysis.
Batch AI Processing Cost Guide: APIs, Spot GPUs, and Pipelines (2026)
Complete cost guide for batch LLM processing — managed batch APIs (OpenAI, Anthropic, Google), self-hosted spot GPU pipelines, queue architecture, and TCO breakdowns.
About these guides
Why GPU and LLM compute costs matter
Compute is the dominant cost in AI development and production. A single H100 SXM instance costs $3.50–$5.50/hr on-demand — a 405B Llama fine-tuning run can consume $5,000–$50,000 in GPU hours depending on technique and provider. LLM API costs scale with token volume: at 100M tokens/day, the difference between GPT-4o ($2.50/1M) and an efficient open-weight model ($0.20/1M) is $690/day — over $250,000/year. Understanding where your compute budget goes, and how to optimise it, is one of the highest-leverage decisions an AI team can make.
GPU cloud vs managed LLM API
The fundamental choice in AI infrastructure is between renting raw GPU compute (and running your own models) versus paying per token through a managed LLM API. GPU cloud gives you full control — any model, any fine-tune, any serving configuration — but requires engineering investment in deployment, autoscaling, and reliability. Managed APIs abstract all of that away at a per-token premium. The break-even point depends on your token volume, model tier, and engineering capacity. These guides provide the data and frameworks to make that decision confidently.
GPU training cost drivers
Training cost is determined by three factors: GPU hours (model size × dataset size × training efficiency), GPU hourly rate (H100 SXM at $3.50–$5.50/hr vs A100 at $1.89–$3.20/hr), and technique (full fine-tune vs LoRA vs QLoRA). A full fine-tune of Llama 3.1 70B on 8× H100s for 24 hours costs $672–$1,056 at on-demand rates. The same run with QLoRA on 4× A100s costs $181–$307. Spot instances can reduce either figure by 40–70%. The GPU guides cover these trade-offs in detail with worked cost examples.
LLM inference cost drivers
LLM inference cost is driven by input/output token ratio, context window length, request volume, and model tier. Output tokens are 3–5× more expensive than input tokens because generation is compute-intensive. Long contexts (100K+ tokens) multiply input cost per request — a 100K-token context at GPT-4o pricing costs $0.25 per call. At 1,000 requests/day that is $2,500/month from context alone. Prompt caching (OpenAI, Anthropic) and batch APIs (50% discount) are the two highest-leverage cost reduction mechanisms for high-volume workloads.
Choosing the right GPU for your workload
GPU selection depends on workload type. LLM training at scale requires high-bandwidth interconnect (NVLink, InfiniBand) and large VRAM — H100 SXM with 80GB HBM3 and 900 GB/s NVLink bandwidth is the standard for 70B+ models. Inference serving prioritises throughput and cost-per-token — A10G and L4 are popular for 7B–13B models where H100 is over-provisioned. Fine-tuning with LoRA/QLoRA can run on RTX 4090s ($0.44–$0.74/hr) for models up to 70B. The GPU guides map workload requirements to the most cost-effective hardware choice.
How GPU cloud pricing has changed in 2026
GPU cloud pricing has become significantly more competitive since 2024. H100 SXM on-demand rates have declined from $8–$10/hr in early 2024 to $3.50–$5.50/hr in mid-2026 as supply from Hopper-generation deployments has increased and Blackwell (B200, GB200) capacity has come online. A100 80GB pricing has fallen below $2/hr at several providers. The entry of hyperscalers (AWS p5, Google A3 Ultra, Azure ND H100 v5) has increased competition with specialist GPU clouds. Spot pricing has become more volatile but averages 50–65% below on-demand across providers.
RAG, embeddings, and vector database costs
Production RAG pipelines have three cost components beyond the LLM inference call: embedding generation (text-embedding-3-small at $0.02/1M tokens, or self-hosted on CPU/GPU), vector database hosting (Pinecone Serverless from $0.096/1M vectors/month, Weaviate Cloud from $0.095/1M vectors/month, or self-hosted Qdrant/Chroma on a $0.10–$0.30/hr CPU instance), and retrieval compute (typically negligible). At 10M daily queries with 5,000-token contexts, embedding costs are $1/day, vector DB hosting is $50–$200/month, and LLM inference is the dominant cost at $500–$5,000/day depending on model tier.