Tensordyne vs Loops
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Tensordyne compared with Loops
| Detail | 
Tensordyne | 
Loops |
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| What it does | Next-generation AI inference systems for speed and efficiency
| Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
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| Stage | Startup
| Not given
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| Founded | 2019
| Not given
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| Based in | US
| Not given
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| Pricing model | Not given
| Not given
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| Pricing | Not given
| Not given
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| Key features | - Logarithmic math architecture for reduced computation
- Multi-trillion parameter mixture of experts support
- Real-time 4K video generation at 30FPS
- Air-cooled design eliminating liquid cooling needs
- Scale-up interconnect for linear model scaling
- 608 PFLOPS of dense compute per rack
- PyTorch, Triton, and vLLM software support
- 16-bit precision for reduced hallucinations
| - forward_backward() operation for computing loss and accumulating gradients
- optim_step() for updating the adapter
- sample() for generating from current or saved policy
- save_state() to save and resume training state
- Tinker API compatibility
- Separate trainer and sampler resources
- Synchronous and asynchronous RL patterns
- Frozen base model weights with LoRA adapter updates
- Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
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| What makes it different | - Energy efficiency through proprietary logarithmic mathematics
- Fastest inference performance available at lowest cost
- Full-stack co-design from math to silicon to systems
- Air-cooled form factor fitting standard datacenters
- Proven silicon with customer validation
| - Managed training and sampling infrastructure, no setup required
- Tinker-compatible API for easy migration
- Ready-made SFT and RL recipes through Loops cookbook
- Support for vision-language models
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 43 ▲ +1 (up) since last check | 77 ● no change since last check |
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| Trust Flow (Majestic) | 18 | 13 |
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| Citation Flow (Majestic) | 36 | 43 |
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| Referring domains (Majestic) | 377 | 550 |
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3 details filled in for both companies.
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