Training Overview

Fireworks helps you fine-tune models to improve quality and performance for your product use cases, without the burden of building & maintaining your own training infrastructure.

Coming from OpenAI? Fireworks uses the same OpenAI-compatible chat completion format for training data — the same messages array with role, content, tool_calls, and weight fields. You can use your existing SFT datasets with no conversion required. See the SFT dataset format for the full schema and examples.

Before managed or dedicated training: account tier and GPU quota

Managed jobs and dedicated Training API runs need training GPU quota, granted automatically by spending tier:

Tier	How to reach it	B200 / B300 (Blackwell)	H200	H100 / A100
No payment method	—	0	0	0
Tier 1	Valid payment method and billing profile	0	16	8
Tier 2	Spend or add $50 in credits	16	16	16
Tier 3	Spend or add $500 in credits	24	24	24
Tier 4	Spend or add $5,000 in credits	32	32	32

Check your quota with the Fireworks CLI (firectl quota list). A job rejected with HTTP 429 quota_exceeded (sometimes a 403 on the job poll) is a tier issue, not a dataset/config problem.

Serverless Training uses a shared pool with its own model, concurrency, and rate limits instead of dedicated training GPU quota.

Need more training quota than your tier allows? Reach out for enterprise support and we’ll help size the right allocation for your workload.

Start here

Use Choose a Training Path to select the right workflow. That page owns the detailed workflow, infrastructure, and interface decisions.

Choose a training path

Compare workflows, infrastructure, and interfaces.

Agent Skills

Configure, run, and troubleshoot training with your agent.

For a side-by-side comparison of SFT, DPO, and RL — the data each needs and what each is good for — see Choose a method.

For custom losses, rollouts, per-step control, or algorithm research, continue to the Training API overview.