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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 →

Team size

Budget

Use case

Stage

Cluster

Stack Layers
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
vLLM
vs
Torchtune

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

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
Field
vLLM
Torchtune
Category
LLM Infrastructure
Fine-tuning
Type
OSS
OSS
Free Tier
✓ Yes
✓ Yes
Plans
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Stars
⭐ 32,000
⭐ 5,200
Health
●75 — Active
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Trajectory
— not enough data
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Synced
today
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vLLM

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

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 Website ↗GitHub ↗
Torchtune Website ↗GitHub ↗

Shared Connections (1)

Unsloth

Only vLLM (12)

LiteLLMOllamaTogether AILlamaIndexModalRunPodAxolotlLlamaFactory

Only Torchtune (1)

vLLM
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