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DATASHEET // TRANSFORMERS

Transformers

The library every LLM ships against first.

OPEN SOURCE2–24 GB VRAMRuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeAPI
Visit TransformersUPDATED 2026-05-21 · DIRECT LINK
github.com/huggingface/transformers
Transformers — preview image

HARDWARE REQUIREMENTS //

Runs locally · Entry GPU (6–8 GB)

2–24 GB VRAM
Min VRAM
2 GB
Rec. VRAM
24 GB
Min RAM
8 GB
Rec. RAM
32 GB
Disk
30 GB
GPU class
Entry GPU
11.8+Apple Silicon ✓CPU-CapableQuant: FP16, BF16, INT8 +2

Scales with the model you load. Tiny LMs run CPU; 70B-class needs 24 GB+ quantized.

[ EDITORIAL PICK ]

Why we recommend Transformers

DERIVED FROM METADATA — NOT SPONSORED
  • Open source

    Source is public — you can audit it, fork it, and you'll never lose access to your workflows if Transformers the company changes direction.

  • Runs on 2 GB

    Fits on entry-level cards (GTX 1660, RTX 3050, RTX 4060). Rare for this category.

  • Apple Silicon

    Native Metal / MPS support — runs on M-series Macs without CUDA gymnastics.

  • Top-tier pick

    Power-user score 94/100 — consistently rated highly by people who use this every day, not just benchmark chasers.

[ EVIDENCE NOTE ]

Documentation-led datasheet

This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.

Training guide →

AT-A-GLANCE SIGNALS //

DERIVED FROM THIS PAGE'S DATA
  • Install difficulty
    Easy

    Runs CPU-only — no CUDA / driver gymnastics required.

  • Hardware comfort
    Entry-level

    Fits on 2 GB cards — GTX 1660 / RTX 3050 territory.

  • Ecosystem
    Strong devkit

    Open-source AND ships an API — easy to integrate, possible to host yourself.

  • Verification
    Recent

    Catalogue entry last updated 56 days ago — re-verification due soon.

[ MORE IN THIS NICHE ]

Other local llm runners tools we rate

Three picks across different tradeoffs — so you don't end up with three near-clones of Transformers.

LIGHTEST HARDWARE //

OpenAI Whisper

The reference open-source speech-to-text model.

OPEN SOURCE2–10 GB VRAM
BEST FREE OPTION //

llama.cpp

The C++ inference engine powering most local LLMs.

OPEN SOURCECPU-CAPABLE
TOP QUALITY //

Ollama

One-command local LLM runtime.

OPEN SOURCECPU-CAPABLE

What is Transformers?

Hugging Face Transformers is the universal Python library for loading and running LLMs (and vision, audio, multimodal models). Slower than vLLM / llama.cpp for serving, but it's where every new model lands first, and the API is the closest thing the field has to a standard.

Pros & cons

✓ PROS

  • Universal loader — every new model lands here first
  • Same API across architectures (LLaMA, Mistral, Qwen, Gemma, etc.)
  • First-class quantization (bitsandbytes, GPTQ, AWQ, FP8)
  • Trainer + PEFT for fine-tuning included

– CONS

  • Slower inference than vLLM / llama.cpp at production scale
  • Library, not a server — you write the orchestration

What's actually free?

Apache 2.0 from Hugging Face.

✓ Actually FreeNo SignupOpen SourceWatermark-Free

Alternatives

llama.cpp

The C++ inference engine powering most local LLMs.

OPEN SOURCECPU-CAPABLE

vLLM

High-throughput LLM serving for GPUs.

OPEN SOURCE24–80 GB VRAM
VRAM fit24–80 GB