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Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
A Japanese AI firm that grew from shogi-AI research into industry ML solutions and a generative-AI platform, HEROZ ASK.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
Open-source library and desktop app for fast, memory-efficient local fine-tuning and inference of open LLMs.
AI research lab building multimodal 'omni' foundation models and infrastructure aimed at robotics and physical-world applications.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦LPU custom inference hardware
- ✦GroqCloud tokens-as-a-service API
- ✦High-speed, low-latency inference
- ✦Pay-as-you-go token pricing
- ✦Free API key to start
- ✦Broad open-model support
- ✦Deep-learning and machine-learning core technology
- ✦HEROZ ASK generative-AI platform
- ✦BtoB and BtoC AI solutions
- ✦BLOOMWORKS product
- ✦Industry AI deployment case studies
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦Optimized LoRA/FFT/PT training kernels for 500+ models
- ✦Local offline model runner for Mac and Windows
- ✦No-code dataset creation from PDFs, CSVs, and JSON
- ✦Unlimited tool-calling and web search inside model runs
- ✦Data Recipes workflow to turn documents into training datasets
- ✦Export to safetensors or GGUF for llama.cpp, vLLM, Ollama
- ✦Multi-GPU support on paid tiers
- ✦Omni multimodal model research and development
- ✦Real-time inference API (Infer) for enterprise use
- ✦Video tagging, search, and clipping infrastructure
- ✦Training data generation from egocentric and robotics footage
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →Deploying generative AI in enterprises
- →Applying ML to industry-specific problems
- →AI-driven business transformation (DX)
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →ML engineers fine-tuning open models on a single GPU for free
- →Teams building custom datasets from unstructured documents
- →Developers wanting to run and compare LLMs fully offline
- →Enterprises needing faster, more accurate multi-node training
- →Powering robotics perception with multimodal AI
- →Running large-scale video search and analysis via API
- →Sourcing specialized training data for frontier AI models