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The article speculates that in five years, frontier AI intelligence could achieve speeds over 5000 tokens per second, faster than human thought, and discusses current AI speeds and video generation capabilities.
Emad Mostaque demonstrated Taalas generating at 14,000 tokens per second, significantly faster than ChatGPT's 50-150 tokens per second, as showcased on The Peter McCormack Show.
NVIDIA has entered full production of Groq 3 LPX AI inference accelerator chips, which supercharge the Vera Rubin platform to achieve the fastest token generation speeds ever recorded for AI models.
NVIDIA is integrating Groq technology into rack-scale products to enhance agentic AI performance by splitting workloads across specialized processors, improving token generation latency and overall responsiveness.
The benchmark compares DFlash2 and MTP techniques in llama.cpp, showing that DFlash2 offers around 20% faster token generation but reduces available context by 38%.
A reflective essay using a blues song as a metaphor for how large language models generate text token by token, arguing that the 'throw' (generation) determines the 'aim' (intention), subverting the usual order of intention before expression.
Nader Dabit visualizes the speed difference between 1,000 tok/s subagents and 85 tok/s, highlighting that lightning skill offload enables ~5x faster execution by using subagents for implementation while keeping frontier models as planners and reviewers.
MiMo v2.5 is praised for its impressive token generation speed in OpenCode, suggesting it's an underrated model update.
Sam Altman announces that a model offering 750 tokens per second will be available for 5.6 SOL in July.
Users report that AI models, including Qwen 3.6 35B, enter infinite loops when integrated with Copilot Chat or Hermes, generating excessive tokens or incorrect tool calls.
Build 9254 of llama.cpp fixes a token generation regression and adds Programmatic Dependent Launch (PDL) support for NVIDIA GPUs, yielding up to 10% speedup in token generation on newer hardware.
A user benchmarks the Nvidia 5090 RTX GPU for LLM inference using llama.cpp, measuring prompt processing and token generation at various power levels, finding that prompt processing is more sensitive to power limits than token generation, and noting differences from the 4090 RTX.
atomic.chat has optimized Gemma 4 26B inference in LLaMA.cpp, achieving ~40% faster token generation on MacBook Pro M5 Max using Multi-Token Prediction (MTP) speculative decoding. This is a notable win for local AI users running desktop apps, coding agents, and private on-device assistants.