Inference 60 (Final Strengthened Version with Explicit Deviation Explanations) All Fields Are Emergent Results of the Joint Action of Force and Entropy Detailed Discussion: In the RECT framework, all fields are not independent fundamental entities, but continuous natural emergences from the joint action of force (balance-rebuilding) and entropy (balance-breaking) across varying reference-object densities, energy scales, and phase intervals. A field is the spatiotemporal distribution of phase gradients ∇Φ. Explicit Replacement of QFT/SM: Traditional QFT and the Standard Model treat fields as a priori fundamental structures and introduce 19+ free parameters, gauge symmetries, and the Higgs mechanism. RECT inverts this: all fields, particles, gauge bosons, statistics, and symmetry breaking in QFT/SM are effective approximations of RECT’s single rule in the current expansion phase under high reference-object density. RECT’s Natural Explanations for Known QFT/SM Deviations: RECT already provides a unified, parameter-free explanation for essentially all known discrepancies or unsolved problems in the Standard Model: .Dark Matter Phenomena: Not new particles, but strong phase gradients from local heat-death black holes (Inferences 11, 14, 60). .Dark Energy / Cosmological Constant Problem: Non-existent; expansion is the natural energy-to-matter phase conversion driven by referential-entropy maximization (Inferences 10, 57). .Hierarchy / Naturalness Problem: Mass is a phase-selection threshold effect; no fine-tuning required (Inferences 36, 49). .Strong CP Problem: CP violation is the inevitable consequence of the entropy-increase direction in the expansion phase (Inference 22). .Neutrino Mass and Oscillation: Arise from phase-selection thresholds at the weak scale (Inferences 36, 47). .Baryon Asymmetry: Naturally generated by chiral preference in entropy-driven phase selection (Inferences 22, 49). .Absence of Quantum Gravity: Fields vanish automatically at the singularity and heat-death limits (∇Φ→0), unifying gravity and quantum effects (Inferences 8, 15–18, 56). .Flavor Puzzle and Fermion Spectrum: Particle generations, masses, and mixing angles are discrete solutions of stable phase windows (Inferences 33–34, 60). RECT’s core promise: Any deviation from QFT/SM can be explained by the same single mechanism (force-entropy-phase gradient) without introducing new particles, fields, or parameters. Mathematical Draft (Strengthened):(photo) Core Statement: Fields are not the “infrastructure” of the universe but the dynamic tapestry woven by force and entropy during mass-energy conversion. QFT/SM is merely the local pattern of this tapestry in the current expansion phase. Connection to Previous Inferences: This inference directly extends Inferences 28, 48, 49, and 56, while providing the field-theoretic foundation for Inference 59 (science itself as emergence).

Its Inference Day at @aiDotEngineer World’s Fair! Catch my talk at 1:30 on the Inference Track (room 2016). I’ll cover what has happened in inference since I published the book. And grab your copy of Inference Engineering at the Baseten booth.

NVIDIA inference software keeps driving down token costs, long after AI infrastructure is deployed. ⚡ In just one month on NVIDIA Blackwell, software optimizations improved DeepSeek V4 performance by up to 5×, reducing token costs to roughly one-fifth of previous levels. NVIDIA's integrated inference software stack compounds improvements across runtimes, kernels, networking, and hardware, delivering up to 20× higher throughput on the same GPU. Co-designed with NVIDIA GPUs, CPUs, networking, and systems, and powered by CUDA-native open source frameworks, NVIDIA's inference software stack ensures new model breakthroughs and optimizations run on NVIDIA from day zero, and keep improving throughput and lowering cost after deployment. See how @Baseten, @Cognition, @DeepInfra, @togethercompute, and @Cursor_ai are turning continuous software innovation into lower cost per token:

Run inference over millions of records — free of SQL, and without your data ever leaving Snowflake. Here's distributed batch inference at scale ⚙️ Title: Batch Inference at Scale URL: ⚙️ Overview A capability that runs distributed inference workloads on Snowpark Container Services (SPCS) with Ray as the execution framework. Inference runs as a dedicated distributed workload, supporting both traditional models and LLMs, consolidating complex operations into a single API call. ❓ Challenges Solved Many customers, especially those migrating from non-SQL systems, need batch inference decoupled from SQL. ・This is especially true for files and unstructured data at large scale ・Rearchitecting workflows around SQL-first patterns is a heavy burden 💡 Methodology & How It Works ・The input DataFrame is materialized and written to a stage as Parquet files ・A job is provisioned on SPCS; the primary node initializes as the Ray head and replicas join as workers ・Each worker reads staged data, performs inference independently, and writes results to an output stage ・Unified API: a single run_batch() call handles both structured and unstructured data ・Multimodal support (images, audio, video); workers load weights once and reuse across batches; JobSpec controls workers and GPU allocation 🌍 Use Cases ・Nightly summarization of millions of support tickets ・Product catalog enrichment via image-to-text generation ・Information extraction from scanned PDFs, audio transcription and labeling, video classification and description BatchInferenceTask integrates with Snowflake Tasks for DAG automation, and all processing stays inside Snowflake — running large-scale inference while preserving data governance. #Snowflake# #BatchInference#

Up to 10x better inference per watt. One-tenth the cost per million tokens. This is what NVIDIA Vera Rubin NVL72 delivers. Join us for the virtual event on June 30 as Dion Harris, Senior Director of HPC and AI Hyperscale Infrastructure Solutions at NVIDIA, and Harsh Banwait, Director of Product at @CoreWeave, explain what it means for the agentic era. #theCUBE# 📆 Save the date:

Paper copies of Inference Engineering are temporarily sold out online, should be restocked early next week. Will have some final copies from this print run with me at AI Engineer World's Fair in SF!

.@ZyphraAI’s AMD-first Inference Cloud is built for long-context, agentic AI, powered by AMD Instinct GPUs and optimized software for scalable open model serving. Follow us as AMD ROCm and AMD Instinct help enable the next wave of AI inference. Learn more: