danielhanchen/unsloth-blackwell-docker

Unsloth Blackwell-Compatible Docker Image (Studio + JupyterLab)

Built by Unsloth. Docker images for 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)

pip install -U huggingface_hub
huggingface-cli login

huggingface-cli 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.

Quick start (lean base image)

huggingface-cli 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:

# pin a CUDA family
UNSLOTH_TORCH_INDEX_FAMILY=cu128 ...
# or a full custom index URL
UNSLOTH_TORCH_INDEX_URL=https://download.pytorch.org/whl/cu128 ...

CPU-only hosts

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

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.