Black Forest Labs
Deploy FLUX image generation models from Black Forest Labs on Together AI. State-of-the-art photorealistic output, open weights, and a production-ready API.
Why Black Forest Labs on Together AI?
Designed for production workloads that need consistent performance and operational control.
State-of-the-art image generation
FLUX leads on photorealism, text rendering, and prompt precision. The same model family powering production image workloads across the industry.
Full control over deployment
Open weights give you complete flexibility — fine-tune FLUX for your style, run it on your own infrastructure, or scale instantly via Together AI's API. No lock-in.
Production workloads at any scale
SOC 2 Type II certified, HIPAA compliant, and deployed on US-based infrastructure. Consistent throughput for high-volume generation pipelines.
Meet the Black Forest Labs family
Explore top-performing models across text, image, video, code, and voice.
Breakthrough technical innovations
Explore all the game-changing architectural advances that make Black Forest Labs models shine.
- Mixture of Experts (MoE)
Sparse expert routing activates only 37B out of 671B parameters for each token in V3. Advanced load balancing without auxiliary losses maintains performance while reducing computational cost.
- Group Relative Policy Optimization
New RL approach that removes separate value networks in RLHF, using grouped relative advantage estimation to cut compute requirements while maintaining training stability.
- Native Reasoning Transparency
First reasoning model to expose complete thinking process in <think> tags. Native reasoning capabilities built into model foundation through large-scale reinforcement learning.
- MetaP Training
First successful implementation of FP8 mixed precision training on a 671B parameter model. Pioneering reinforcement learning approach without supervised fine-tuning as preliminary step.
- Multi-Head Latent Attention
Innovative attention mechanism that reduces KV-cache memory requirements while maintaining modeling performance. Optimized for efficient inference deployment.
- Multi-Token Prediction
Novel training objective that allows the model to predict multiple tokens simultaneously. Enhanced performance and efficiency through advanced training techniques.
Deployment options
Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.
Real-time
A fully managed inference API that automatically scales with request volume.
Best for
Batch
Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.
Best for
Dedicated Model Inference
An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
Best for
Dedicated Container Inference
Run inference with your own engine and model on fully-managed, scalable infrastructure.
Best for