Long‑context AI efficiency

Long‑Context AI Efficiency Layer for Large Language Models.

SpiralThink is an efficiency layer for large language models that reduces inference cost, unlocks longer context, and extends the life of existing GPUs and servers without changing your backbone models.

Talk to our team See the economics
Up to 25% lower compute on long‑context workloads.
50–70% less KV‑cache pressure on existing GPUs.
Drop‑in layer for Llama‑class models and serving stacks.
Product

What SpiralThink is.

SpiralThink is a geometric latent‑space layer that compresses context into compact blocks, reasons over this compressed view, and feeds it back into your backbone model. The result: longer effective context, lower cost, and more robust behavior on real‑world prompts.

Geometric context compression

SpiralThink groups tokens into blocks and builds a low‑dimensional latent summary for each block. The reasoning core sees a compressed version of the sequence instead of every token at full resolution.

Blocks of text → latent vectors
Reduced effective sequence length

Latent‑space reasoning

A compact transformer operates on this latent sequence, capturing global structure and long‑range dependencies at a fraction of the cost of full‑sequence attention.

Lightweight latent core
Optimized for long documents

Robust predictions under noise

Structured regularization during training helps the model stay stable when inputs are messy, reordered, or partially redundant—exactly how documents look in production.

Consistency‑driven training
Less brittle on real prompts
Economics

Built for real AI unit economics.

Long‑context workloads amplify every inefficiency in your stack. SpiralThink helps you serve these workloads on your existing hardware and with clearer, more predictable cost per token.

What changes with SpiralThink

  • • More concurrent users on the same GPU fleet, by reducing KV‑cache pressure.
  • • Longer documents per request without forcing a full hardware refresh.
  • • Clearer cost per 1M tokens on long‑context traffic, easier to explain to finance.
  • • A path from “pilot only” to always‑on production for complex reasoning workloads.
Labs

SpiralThink Labs.

SpiralThink Labs explores the frontier of efficient reasoning: beyond larger models, towards smarter use of computation across latent spaces and diverse hardware.

Latent‑space architectures
Research into geometric representations of long sequences, allowing models to maintain a global view without paying quadratic attention costs.
Robustness under real‑world noise
Experiments with structured perturbations and consistency training, so models remain reliable when prompts are messy, incomplete, or duplicated.
Deployment on heterogeneous hardware
Prototypes for running long‑context reasoning on mixed fleets of modern and legacy GPUs, CPUs, and edge devices—without rewriting your entire model lineup.
For research and co‑development inquiries, contact us at hello@SpiralThink.com.
Contact

Talk to the SpiralThink team.

Whether you are evaluating long‑context workloads, modernizing an existing GPU fleet, or building an AI platform, we would be happy to explore how SpiralThink can help.

For product questions, pilots, or partnership opportunities, reach out to us directly.

Email hello@SpiralThink.com