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207 tools · 25 stacks

AI tools are all over the place. This is the full landscape — 207 tools across 17 categories, mapped and connected. Ready to narrow it down? Build your stack →

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What are you building and how is it defined?
How do you write and ship code?
How does your AI think and act?
Which models and infrastructure power it?
How do you build, observe, and extend it?
These tools integrates with
Torchtune
vs
vLLM

Choose Torchtune when…

  • •You want pure PyTorch with no abstraction layers over training
  • •You're primarily working with Meta's Llama models
  • •Reproducibility and research clarity are priorities

Choose vLLM when…

  • •You're serving LLMs at high throughput in production
  • •Continuous batching and PagedAttention are needed
  • •You're running your own GPU inference cluster
Field
Torchtune
vLLM
Category
Fine-tuning
LLM Infrastructure
Type
OSS
OSS
Free Tier
✓ Yes
✓ Yes
Plans
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—
Stars
⭐ 5,200
⭐ 32,000
Health
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●75 — Active
Trajectory
— not enough data
— not enough data
Synced
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today

Torchtune

Meta's official fine-tuning library. Pure PyTorch — no abstraction layers. Supports LoRA, QLoRA, and full fine-tuning for Llama models. Designed for reproducibility and research.

vLLM

Production-grade LLM inference server. PagedAttention enables high throughput and efficient KV cache memory management.

Torchtune Website ↗GitHub ↗
vLLM Website ↗GitHub ↗

Shared Connections (1)

Unsloth

Only Torchtune (1)

vLLM

Only vLLM (12)

LiteLLMOllamaTogether AILlamaIndexModalRunPodAxolotlLlamaFactory
See full comparison in Explore →