Fractile

10/10Traction

Fractile is developing specialized in-memory computing hardware that performs all operations required for large language model inference directly within memory, eliminating the constant movement of model weights between memory and processor. This architecture aims to drastically reduce the memory bottleneck that limits LLM inference speed and efficiency, enabling faster and more cost-effective deployment of large transformer networks.

London, United Kingdom · HQ

Founded 202213920K+ followers

Semiconductor
Artificial Intelligence
Hardware

Updated yesterday

Funding

Raised to date

$967.8MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

across 3 rounds

£577M · Series C

Announced Oct 2026

Disclosed cumulative: $967.8M

3/3

RoundDateAmountValuationInvestors

Founders

2 founders

Walter Goodwin

CEO

James Scales

Fractile

Product

Problem

Existing hardware architectures create a significant bottleneck for large language model (LLM) inference because they require constant movement of model weights between memory and the processor, consuming the majority of compute time. This memory bottleneck limits the speed and efficiency of LLM inference, increasing costs and hindering real-time applications.

Solution

Fractile is developing specialized hardware that performs all operations required for LLM inference directly within memory, eliminating the need to move model weights to the processor. This in-memory computing approach drastically reduces the memory bottleneck, enabling faster and more efficient inference for large transformer networks. Fractile's technology aims to achieve significantly faster inference speeds at a fraction of the cost compared to current GPU-based systems. By removing the memory bottleneck, Fractile unlocks new possibilities for real-time LLM applications and enables the deployment of larger, more complex models.

Target Audience

The primary target audience includes organizations and researchers working with large language models who require faster, more efficient, and cost-effective inference solutions.

Features

In-memory computing architecture that eliminates the memory bottleneck in LLM inference
Specialized hardware designed for efficient execution of all operations required for LLM inference
Optimized for large transformer networks
Achieves significantly faster inference speeds compared to traditional GPU-based systems
Reduces the cost of LLM inference