The Race to Sub-Penny Inference
The AI industry is aggressively attacking the compute bottleneck through multiple vectors—direct-to-silicon hardwiring, extreme model distillation, KV cache compaction, and prompt caching—to drive inference costs toward zero and enable real-time agentic workflows.
Trajectory
This is the technical counter-narrative to the 'Trillion Dollar CapEx' arc. Builders are finding ways to bypass massive cloud costs by shrinking models (0.6B replacing 120B), optimizing memory, and building custom inference silicon.
Timeline (25 events)
A Theoretical Framework for Modular Learning of Robust Generative Models
Theoretical framework proving modular LLM training (combining small expert models via gating) can match monolithic models directly advances the sub-penny inference arc. This is a concrete algorithmic approach to reducing compute costs — one of multiple vectors (alongside distillation, KV compaction, prompt caching) attacking the inference cost bottleneck.
Brain inspired machines are better at math than expected
The attack on compute costs is moving beyond software optimization into radical architectural shifts. Discrete ±1 neural networks and neuromorphic chips solving complex physics equations demonstrate how the industry is attempting to bypass traditional von Neumann energy bottlenecks entirely.
Learning with Boolean threshold functions
The attack on compute costs is moving beyond software optimization into radical architectural shifts. Discrete ±1 neural networks and neuromorphic chips solving complex physics equations demonstrate how the industry is attempting to bypass traditional von Neumann energy bottlenecks entirely.
I made a local AI creature that runs on integers
The attack on compute costs is moving down the stack. We are seeing extreme integer-based quantization for local models, and product companies like Cursor bypassing standard infrastructure to write their own custom low-level GPU kernels for massive efficiency gains.
1.5x faster MoE training with custom MXFP8 kernels
The attack on compute costs is moving down the stack. We are seeing extreme integer-based quantization for local models, and product companies like Cursor bypassing standard infrastructure to write their own custom low-level GPU kernels for massive efficiency gains.
Sink-Aware Pruning for Diffusion Language Models
The attack on compute costs is moving down the stack. We are seeing extreme integer-based quantization for local models, and product companies like Cursor bypassing standard infrastructure to write their own custom low-level GPU kernels for massive efficiency gains.
Github: When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models AKA Inheritune
The cost collapse is happening via two vectors simultaneously: China's aggressive API price war ('giveaway war') forcing cloud costs down, and hardware innovations like ChatJimmy's model-on-silicon ASIC achieving 15k tokens/sec.
I evaluated LLaMA and 100+ LLMs on real engineering reasoning for Python
The cost collapse is happening via two vectors simultaneously: China's aggressive API price war ('giveaway war') forcing cloud costs down, and hardware innovations like ChatJimmy's model-on-silicon ASIC achieving 15k tokens/sec.
China’s AI Giveaway War - The Information
The cost collapse is happening via two vectors simultaneously: China's aggressive API price war ('giveaway war') forcing cloud costs down, and hardware innovations like ChatJimmy's model-on-silicon ASIC achieving 15k tokens/sec.
15,000+ tok/s on ChatJimmy: Is the "Model-on-Silicon" era finally starting?
The cost collapse is happening via two vectors simultaneously: China's aggressive API price war ('giveaway war') forcing cloud costs down, and hardware innovations like ChatJimmy's model-on-silicon ASIC achieving 15k tokens/sec.
Hardware LLM at 16K Tokens/s
Custom silicon is moving from theory to production, with Taalas unveiling hardware capable of 16,000 tokens per second, drastically bending the inference cost and speed curves.
Taalas serves Llama 3.1 8B at 17,000 tokens/second
The assault on inference costs is accelerating through extreme hardware and software optimization. Taalas hitting 17,000 tokens/second via custom silicon and the introduction of 'Looped Language Models' that decouple reasoning from parameter size represent massive structural bends in the compute cost curve.
Qwen3 coder next oddly usable at aggressive quantization
The assault on inference costs is accelerating through extreme hardware and software optimization. Taalas hitting 17,000 tokens/second via custom silicon and the introduction of 'Looped Language Models' that decouple reasoning from parameter size represent massive structural bends in the compute cost curve.
LLMs don’t need more parameters; they need "Loops." New Research on Looped Language Models shows a 3x gain in knowledge manipulation Compared to Equivalently-sized Traditional LLMs. This proves that 300B-400B SoTA performance can be crammed into a 100B local model?
The assault on inference costs is accelerating through extreme hardware and software optimization. Taalas hitting 17,000 tokens/second via custom silicon and the introduction of 'Looped Language Models' that decouple reasoning from parameter size represent massive structural bends in the compute cost curve.
Quoting Thibault Sottiaux
The assault on inference costs is accelerating through extreme hardware and software optimization. Taalas hitting 17,000 tokens/second via custom silicon and the introduction of 'Looped Language Models' that decouple reasoning from parameter size represent massive structural bends in the compute cost curve.
Free open-source prompt compression engine — pure text processing, no AI calls, works with any model
The assault on inference costs is accelerating through extreme hardware and software optimization. Taalas hitting 17,000 tokens/second via custom silicon and the introduction of 'Looped Language Models' that decouple reasoning from parameter size represent massive structural bends in the compute cost curve.
ThunderKittens 2.0: Even Faster Kernels for Your GPUs
Hardware startups like Taalas are bypassing general-purpose compute entirely to bake LLMs into silicon, while software optimizations like 1-bit BitNet are achieving massive token speeds on consumer edge devices.
I got 45-46 tok/s on IPhone 14 Pro Max using BitNet
Hardware startups like Taalas are bypassing general-purpose compute entirely to bake LLMs into silicon, while software optimizations like 1-bit BitNet are achieving massive token speeds on consumer edge devices.
The path to ubiquitous AI
Hardware startups like Taalas are bypassing general-purpose compute entirely to bake LLMs into silicon, while software optimizations like 1-bit BitNet are achieving massive token speeds on consumer edge devices.
16.000 tokens/second - Taalas: LLMs baked into hardware. No HBM, weights and model architecture in silicon
Hardware startups like Taalas are bypassing general-purpose compute entirely to bake LLMs into silicon, while software optimizations like 1-bit BitNet are achieving massive token speeds on consumer edge devices.
Taalas HC1: The Chip That Can't Change Its Mind
Hardware startups like Taalas are bypassing general-purpose compute entirely to bake LLMs into silicon, while software optimizations like 1-bit BitNet are achieving massive token speeds on consumer edge devices.
Quoting Thariq Shihipar
This is the technical counter-narrative to the 'Trillion Dollar CapEx' arc. Builders are finding ways to bypass massive cloud costs by shrinking models (0.6B replacing 120B), optimizing memory, and building custom inference silicon.
Fast KV Compaction via Attention Matching
This is the technical counter-narrative to the 'Trillion Dollar CapEx' arc. Builders are finding ways to bypass massive cloud costs by shrinking models (0.6B replacing 120B), optimizing memory, and building custom inference silicon.
We replaced the LLM in a voice assistant with a fine-tuned 0.6B model. 90.9% tool call accuracy vs. 87.5% for the 120B teacher. ~40ms inference.
This is the technical counter-narrative to the 'Trillion Dollar CapEx' arc. Builders are finding ways to bypass massive cloud costs by shrinking models (0.6B replacing 120B), optimizing memory, and building custom inference silicon.
The path to ubiquitous AI (17k tokens/sec)
This is the technical counter-narrative to the 'Trillion Dollar CapEx' arc. Builders are finding ways to bypass massive cloud costs by shrinking models (0.6B replacing 120B), optimizing memory, and building custom inference silicon.