AI-Native OS at Planetary Scale
Unifies geo-distributed, heterogeneous GPUs into one elastic fabric for Efficient AI
/ 01
High Performance
High-performance framework to aggregate geo-distributed GPUs and deliver unprecedented throughput on heterogeneous compute resources.
/ 02
Affordability and Accessibility
Enable AI training and Inference on a wide spectrum of GPUs ranging from commodity to high-end ones with limited compute and memory capacity.
/ 03
LLM at Any Scale
Built-in support and optimization for major LLMs and highly customizable for new LLMs in elastic ways.
Use Case: Post-training and RL
16 H100 nodes to RL DeepSeek-R1.
50%
of typical required hardwareAgentic RL - 3x speedup compare to NeMo Aligner
Use Case: AIGC - Image/Video Gen
70%
GPU memory reduction from Quantization without quality loss3-10x
speedup via Quantization + OptimizationUse Case: Serving
Elastic Deployment: autoscaling across H100s and H200s in multi-region
99.99%
reliability (vs 99.5%
neo cloud) with 2x cost saving (vs Hyperscaler)/ 01
High Performance
High-performance framework to aggregate geo-distributed GPUs and deliver unprecedented throughput on heterogeneous compute resources.
Use Case: Post-training and RL
16 H100 nodes to RL DeepSeek-R1.
50%
of typical required hardwareAgentic RL - 3x speedup compare to NeMo Aligner
/ 02
Affordability and Accessibility
Enable AI training and Inference on a wide spectrum of GPUs ranging from commodity to high-end ones with limited compute and memory capacity.
Use Case: AIGC - Image/Video Gen
70%
GPU memory reduction from Quantization without quality loss3-10x
speedup via Quantization + Optimization/ 03
LLM at Any Scale
Built-in support and optimization for major LLMs and highly customizable for new LLMs in elastic ways.
Use Case: Serving
Elastic Deployment: autoscaling across H100s and H200s in multi-region
99.99%
reliability (vs 99.5%
neo cloud) with 2x cost saving (vs Hyperscaler)PRODUCT
Quantization
3-10x speedup
Compress large models for fast inference without losing accuracy
- 4-bit quantization
- Kernel-level optimization
- Reduce memory by 4x
- Software-hardware co-design
Elastic Deployments
99.99% Reliability
Dynamically autoscale application based on traffic
- SLO‑aware and inter-cloud
- GPU workers spin-up within seconds
- Failure auto detection
- Observability and traceability
Cloud GPUs(PODS)
60% Cost Reduction
Launch AI applications on containerized GPUs
- Programmable GPUs with Pod APIs
- Persistent volumes for high-speed I/O
- Spot/On-demand/Reservation
Model APIs
10x Cheaper
Serverless Endpoint compatible with OpenAI API standard
- Multi-modal: Text, Image, Video, Audio
- Content generation for less one 1 sec
- Model customization with pluggable LoRA
OPEN SOURCE
BloomBee: Run large language models in a heterogeneous
decentralized environment with offloading
decentralized environment with offloading
RECENT BLOG POSTS
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