What does it cost to train a 70B parameter model in 2026?
Realistic 2026 numbers for training a 70B-parameter LLM from scratch: GPU-hours, $/token, cluster size, and total spend across CoreWeave, AWS, Azure, Lambda and RunPod.
Training a 70B-parameter dense transformer from scratch in 2026 lands between $1.8M and $4.5M depending on dataset size, cluster type, and provider. Here is the math, with numbers from the Servers.computer index.
The reference run
Industry-standard 70B training targets roughly 15 trillion tokens — Chinchilla-optimal at 20:1 tokens-to-params, plus headroom. At 8x H100 SXM5 throughput (~7.9 PFLOPS dense FP8, ~50% MFU in practice), that is roughly 1.1M H100-hours, or ~140 days on a single 8-GPU node.
Realistic cluster sizes
- 128x H100 (16 nodes): ~17 days wall-clock. Compute spend ≈ $1.65M–$2.2M.
- 256x H100 (32 nodes): ~9 days wall-clock. Compute spend ≈ $1.7M–$2.3M (scaling overhead eats some).
- 128x B200 (16 nodes): ~12 days wall-clock. Compute spend ≈ $2.4M–$2.9M.
Where the rest of the spend goes
Compute is 60–75% of total spend. The remainder breaks down as: storage and egress (~8%), checkpointing infrastructure (~5%), debug/restart runs from failed jobs (~10%), and salaries / infra engineering (the part nobody puts in the spreadsheet).
The cheapest credible path
RunPod EU-West-1 H100 PCIe at $18.40/hr, reserved CoreWeave H100 SXM at ~$22/hr, and Lambda 1-Click Clusters at $24.50/hr are today's cost frontier for committed training. Hyperscalers (AWS P5, Azure ND H100 v5) trade ~20% premium for region depth, compliance, and managed networking — usually worth it for funded teams.
Use the calculator
Servers.computer's training cost calculator at /ai-training-cost-calculator plugs your parameter count, token budget, and provider into live pricing — useful for sanity-checking a board deck or a runway model.