Gemini tops benchmarks, again

2026-02-24 · Source: Ben's Bites · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

Google has released Gemini 3.1 Pro, which performs strongly on reasoning tasks and SVG creation, though it faces speed issues and some users reported account bans when using it with OpenClaw. Meanwhile, Taalas, a 2.5-year-old hardware startup, developed a chip with Llama 3.1 weights baked in, achieving ~17,000 tokens/second output speeds, significantly faster than Groq's ~600 tokens/second and Cerebras's ~2,000 tokens/second, despite lower quality quantization. OpenAI is partnering with major consulting firms like BCG and McKinsey to promote its "Frontier" platform for enterprise AI coworkers. Additionally, Claude Code has introduced updates including Git worktree support, app preview for desktop, and a beta security scanning feature. Recall.ai powers many meeting AI applications, handling recording data across various platforms.

Key takeaway

For Machine Learning Engineers evaluating LLM deployment strategies, the emergence of specialized hardware like Taalas's silicon Llama demonstrates a path to significantly higher inference speeds and lower costs. You should investigate these hardware-baked solutions for applications demanding extreme throughput, even if it means initially accepting some quality compromises, as the underlying technology promises rapid evolution towards frontier model capabilities.

Key insights

Hardware-accelerated LLMs offer significant speed advantages over traditional inference methods, despite potential initial quality trade-offs.

Principles

• Hardware-software co-design can drastically improve LLM inference speed.
• Distillation is a common technique to transfer capabilities to smaller models.

Method

Taalas bakes Llama 3.1 weights directly into hardware, enabling ~17,000 tokens/second output, supporting custom context windows and LoRA fine-tuning for efficient, high-speed inference.

In practice

• Consider hardware-optimized LLMs for high-throughput applications.
• Explore Claude Code's new features for agentic development workflows.

Topics

• Gemini 3.1 Pro
• AI Hardware Acceleration
• LLM Benchmarking
• Model Distillation Attacks
• Enterprise AI

Code references

• RhysSullivan/fastergh
• T3-Content/quipslop
• newmaterialco/chowder-iOS
• wesm/agentsview
• CleverCloud/mdr

Best for: Machine Learning Engineer, NLP Engineer, Investor, AI Engineer, Software Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ben's Bites.