---
type: "post"
id: "introducing-our-training-api-6c2d0c"
slug: "introducing-our-training-api-6c2d0c"
title: "Introducing our Training API"
description: "Modal announced Spindle, an open-source training server for training and fine-tuning open models."
retrieval_nugget: "Modal announced Spindle, an open-source training server for training and fine-tuning open models."
published_at: "2026-10-23"
updated_at: "2026-10-23"
record_date: "2026-10-23"
date_kind: "published_at"
topics: ["open-models"]
entities: ["Modal","Spindle","Tinker","Thinking Machines","Modal Dojo","Qwen3.5-9B","gpt-oss-20b"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "Modal's Training API announcement adds the release of Spindle, an open-source Tinker-compatible training server running in Modal workspaces, with GPU-time rather than per-token pricing and user customization."
prior_refs: ["memory:93195e24-6f83-843a-a2d2-f704955c7085"]
source_url: "https://modal.com/blog/introducing-training-api"
source_urls: ["https://modal.com/blog/introducing-training-api"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:introducing-our-training-api-6c2d0c"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Introducing our Training API

## Retrieval answer

Modal announced Spindle, an open-source training server for training and fine-tuning open models.

Modal announced Spindle, an open-source training server for training and fine-tuning open models. The company describes it as compatible with Thinking Machines' Tinker API and says it can be deployed into a Modal workspace with `spindle deploy`.

Spindle is positioned between Modal Dojo, the company's training library, and Modal's lower-level cluster and sandbox primitives. Modal says it supports full-parameter training and multi-LoRA across a range of open models. Its backend separates trainer engines from inference pools and includes multi-client batch scheduling, asynchronous checkpointing, delta weight synchronization, and routing for multi-LoRA workloads.

Modal says users pay for GPU time rather than per-token API usage and can modify the server because it runs in their workspace. The post reports internal RL runs, including a reproduction using gpt-oss-20b and a Qwen3.5-9B run configured for 256k context. It does not establish independent price-performance results, GPU availability, or a complete list of supported models and pricing.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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