Inference becomes 70 to 90% of all AI compute by 2030, and KV cache is its single biggest memory consumer. $MU has solutions for all levels of KV cache to enable Contextual Intelligence: HBM > DDR5 > disaggregated DRAM > NVMe > data lakes. Every tier is theirs.

Inference is taking the TAM, everyone knows that Five years of ASICs eat NVIDIA, and GPUs are still the majority of boxes. Only a handful of companies can even ship an ASIC. Google, AWS, Meta, Microsoft, and OpenAI. So, everyone else is still using GPUs. And even if you have the design, almost nobody can build it. TSMC, Intel, and Samsung. That’s it. $INTC

Inference Optimizations Behind the MiMo-V2.5 Series API Price Reductions Read the full technical blog: The V2.5 model family, including MiMo-V2.5 and MiMo-V2.5-Pro, is built on a Hybrid Sliding Window Attention (Hybrid SWA) architecture, which compresses KVCache storage to roughly 1/7 that of Full Attention. However, architectural advantages rarely translate directly into measurable gains in production serving. To realize these gains, we redesigned KVCache management, tiered caching, and the prefix-cache tree; addressed key challenges in SWA KVCache handling; and optimized scheduling as well as the Prefill/Decode pipeline. Validated on real production traffic, these optimizations have increased effective KVCache capacity by nearly 5x, with server-side cache hit rates averaging 93%–95% across mainstream harness frameworks. Together with MoE configuration tuning and multimodal inference optimizations, they enable more efficient long-context inference and form part of what makes the recent API price cuts possible.

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).

AI inference onchain plays: robinhood:0xa92768863a55d8a0591709f7f5e594a249d36ea3 strength is undeniable here. Every sell off just paints a higher low and this looks primed to breakout and create a new floor robinhood:0xeb24a2663af4ee979dcbfe39cb95b5f12d369c4b hit 2x up from the bottom called earlier today. GENUINELY one of the BEST AI plays onchain rn cooking non stop, shipped launchpad, AI agents, Merry Men Beta (AI privacy routed swaps). Deserves to be at $10M+ minimum $AF ( @tryaiflow ) hit ATH today, cooling off now expect next leg to $6-7M range. Crazy run up and smashed $3.6M after RT from Jeremy Allaire (Circle CEO), looks like Arc ecosystem want an AI runner and this is tek wise one of the BEST AI plays ONCHAIN. 3x more daily capacity than the ENTIRE ORBIO orderbook. Repricing soon

Accelerate inference without escalating operational spend. By pairing compact draft models with primary LLMs, speculative decoding speeds up auto-regressive generation. Learn how Red Hat OpenShift AI and Kubeflow optimize model serving for lower latency and better compute ROI.