AI Technologies at the Turn of Q2 2026: TurboQuant, Gemma 4, and Claude Mythos Preview

Between late March and early April 2026, three AI announcements appeared that are worth knowing – but before we jump into testing, we need to look at the broader context and possible challenges.

TurboQuant – Compressing the KV Cache

TurboQuant is a compression algorithm presented by Google Research on March 25, 2026. It targets the key-value (KV) cache, one of the main memory bottlenecks in LLM inference. Google reports that the cache can be quantized to about 3 bits per value without loss of quality, cutting KV-cache memory by at least 6x, with up to 8x faster attention on NVIDIA H100 GPUs[1][2]. Importantly, it doesn't require retraining or calibration data – it's applied at inference time. The open question is how quickly it lands in popular serving frameworks and how it behaves in your own production workloads.

Gemma 4 – Google Focuses on Locality

Gemma 4, released on April 2, 2026, is a family of four open models: E2B and E4B for phones and edge devices, a 26B Mixture-of-Experts model with about 3.8B active parameters, and a 31B dense model[3]. For the first time it ships under the Apache 2.0 license and supports text, image, audio, and video input[3]. This is a step towards a "private AI assistant" that doesn't need a cloud connection for daily work on code. The challenge will be to keep strong reasoning while keeping energy consumption low.

Claude Mythos Preview – Too Capable to Release Broadly?

On April 7, 2026, Anthropic announced Claude Mythos Preview – but instead of a public launch, it made the model available only to selected partners through Project Glasswing, a defensive cybersecurity initiative[4]. According to Anthropic, the model has already found thousands of high-severity vulnerabilities, including in major operating systems and browsers[4]. It's an unusual signal: a frontier lab deciding that a model's capabilities justify restricted access. For the rest of the industry, the question is how long such restrictions can hold as open-source competition keeps catching up.

Summary

2026 may bring greater accessibility to advanced models, but along with it come challenges: infrastructure costs, integration complexity, security, and refining production processes. Before implementing any of these new developments, it's worth running reliable tests on your own data sets.

Sources

1. TurboQuant: Google's KV Cache Optimization Explained – Analytics Vidhya
2. Did Google Just Ease the AI Memory Bottleneck? – HPCwire (March 31, 2026)
3. Google releases Gemma 4: Four open models with Apache 2.0 license – DataNorth
4. Project Glasswing – Anthropic (April 7, 2026)