---
license: agpl-3.0
language:
- en
library_name: docker
tags:
- docker
- unsloth
- blackwell
- fine-tuning
- lora
- llm
- gpu
- jupyter
---

# Unsloth Blackwell-Compatible Docker Image (Studio + JupyterLab)

Built by Unsloth. Docker images for [Unsloth](https://github.com/unslothai/unsloth) that run on every current NVIDIA datacenter and consumer GPU from Turing through Blackwell, on `linux/amd64`.

This build bundles the work from three in-flight pull requests:

- the Blackwell base + Studio two-image build (`unslothai/unsloth#5748`),
- the Colab-grade JupyterLab UX (`unslothai/unsloth#6681`),
- the `UNSLOTH_TORCH_INDEX_URL` / `_FAMILY` torch-index override (`unslothai/unsloth#6692`).

> All images and outputs in this repository are licensed under the GNU AGPLv3. Copyright 2026-Present the Unsloth team. Source: https://github.com/unslothai/unsloth Website: https://unsloth.ai

## Two images

| Tarball | Image | Use |
|---|---|---|
| `unsloth-blackwell-studio.tar.gz` | full Studio + JupyterLab + sshd | the default. Unsloth Studio on `:8000`, JupyterLab (Unsloth Dark theme, the unslothai/notebooks collection) on `:8888`. |
| `unsloth-blackwell-base.tar.gz` | lean base | training/CLI only, no web UI. Smaller. |

## What's in the image

| Component | Version |
|---|---|
| Base image | `nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04` |
| PyTorch | `2.10.0+cu128` |
| Triton | `3.6.0` |
| xformers | `0.0.34` (cu128) |
| bitsandbytes | `0.49.2` |
| vLLM | `0.19.1` |
| flashinfer | `0.6.6` |
| Unsloth | `2026.6.9` |
| Unsloth Zoo | `2026.6.7` |
| transformers | `5.12.1` |
| trl | `0.24.0` |
| peft | `0.19.1` |
| accelerate | `1.14.0` |
| JupyterLab | `4.6.0` (notebook `7.6.0`) |
| Built-in SASS | `sm_75 sm_80 sm_86 sm_89 sm_90 sm_100 sm_120` |

## Supported GPUs

| Compute Cap | GPU family | Examples | Status |
|---|---|---|---|
| sm_75 | Turing | T4, RTX 20-series, Quadro RTX | Works (no bf16, falls back to fp16) |
| sm_80 | Ampere DC | A100, A30 | Native SASS |
| sm_86 | Ampere | RTX A6000, A40, RTX 30-series | Native SASS |
| sm_89 | Ada Lovelace | L4, L40, L40S, RTX 40-series | JIT-PTX from sm_86 |
| sm_90 | Hopper | H100, H200, GH200 | Native SASS |
| sm_100 | Blackwell DC | B100, B200, GB200 | Native SASS |
| sm_103 | Blackwell DC | B300, GB300 | JIT-PTX from sm_100 |
| sm_120 | Blackwell consumer | RTX 50-series, RTX PRO 6000 Blackwell | Native SASS |
| sm_121 | Blackwell | GB10 (DGX Spark) | JIT-PTX from sm_120 |

DGX Spark (GB10) is an ARM host; this image is `linux/amd64` only.

## Quick start (full Studio + JupyterLab image)

```bash
pip install -U huggingface_hub
hf auth login

hf download danielhanchen/unsloth-blackwell-docker \
    unsloth-blackwell-studio.tar.gz --local-dir /tmp

gunzip -c /tmp/unsloth-blackwell-studio.tar.gz | docker load
docker images unsloth-blackwell:studio

# Studio on :8000, JupyterLab on :8888 (a first-boot password is printed in the logs)
docker run --gpus all -p 8000:8000 -p 8888:8888 unsloth-blackwell:studio
```

For a public JupyterLab link over Cloudflare, add `-e UNSLOTH_JUPYTER_CLOUDFLARE=1`.

JupyterLab opens on the categorized `Unsloth Notebooks` view (Main, Gemma, GRPO, Tool Calling, Vision, AMD, and more), refreshed from [unslothai/notebooks](https://github.com/unslothai/notebooks) on each boot while preserving your edits.

## Quick start (lean base image)

```bash
hf download danielhanchen/unsloth-blackwell-docker \
    unsloth-blackwell-base.tar.gz --local-dir /tmp
gunzip -c /tmp/unsloth-blackwell-base.tar.gz | docker load

# 5-step LoRA smoke test on Llama-3.2-1B-4bit
docker run --rm --gpus all unsloth-blackwell:base python /workspace/smoke_test.py
```

## Torch-index override (PR #6692)

Pin which PyTorch wheel index the installers use, instead of letting the host GPU decide. Useful for headless / CI / air-gapped builds and custom mirrors. An explicit pin wins verbatim and skips all GPU probing:

```bash
# a CUDA family: cu126, cu128, cu130, ... (or cpu)
UNSLOTH_TORCH_INDEX_FAMILY=cu128 ...
# an AMD ROCm family (native install on AMD hosts): rocm7.2, gfx1151, gfx120X-all, ...
UNSLOTH_TORCH_INDEX_FAMILY=rocm7.2 ...
# or a full custom index URL, used exactly as given
UNSLOTH_TORCH_INDEX_URL=https://download.pytorch.org/whl/cu128 ...
```

The Studio installer records the index it installed from, so a later `unsloth studio update` that changes the pin reinstalls torch from the new index instead of keeping the old wheel.

## CPU-only hosts

Training needs an NVIDIA GPU, but JupyterLab, the GGUF tooling (baked llama.cpp) and Studio chat work on CPU:

```bash
docker run -e UNSLOTH_ALLOW_CPU=1 -p 8000:8000 -p 8888:8888 unsloth-blackwell:studio
```

## License

GNU AGPLv3. The image surfaces its license and attribution in JupyterLab (Help > About Unsloth Docker Studio) and on the login screen. Copyright 2026-Present the Unsloth team.