@telegraph.co.uk @thetimes.com

Special Revelation

The God-given/inspired name as a
form of special revelation. An act of predestined planning.

#Inference #DeductiveLogic
#DeductiveReasoning
#EpistemicJustification

en.wikipedia.org/wiki/Special...

d-Matrix, Gimlet Labs Partner to Boost Agentic AI Inference Performance AI infrastructure startup d-Matrix and applied AI firm Gimlet Labs have teamed up to bring specialized inference hardware into AI cloud environments, aiming to boost the performance and energy efficiency for real-time, agentic workloads. Under the partnership, Gimlet plans to integrate d-Matrix Corsair accelerators into Gimlet Cloud alongside traditional GPUs. In the hybrid architecture, GPUs handle compute-heavy stages of inference, while memory- and latency-sensitive operations are routed to Corsair. The companies say this split can deliver up to 10x improvements in latency and throughput per watt compared with GPU-only deployments. “If you can fundamentally change how people interact with AI, they’ll be much more engaged,” said Zain Asgar, co-founder and CEO of Gimlet Labs, during a press briefing. “We want to enable real-time interaction with AI systems, and that starts with designing hardware and software for the workloads that matter most.” Heterogeneous Infrastructure for Real-Time AI The partnership reflects a growing trend toward multi-silicon AI infrastructure – combining GPUs with inference accelerators and other specialized chips to optimize performance and efficiency. “Inference is never a one-size-fits-all problem. Heterogeneity is the path forward,” said d-Matrix CEO Sid Sheth. “From day one, d-Matrix has focused on inference, and with power limits capping...

d-Matrix, Gimlet Labs Partner to Boost Agentic AI Inference Performance
->Data Center Knowledge | More on "AI inference hardware energy efficiency" at BigEarthData.ai | #Inference #ArtificialIntelligence #AI

AMD’s Ryzen AI NPUs Can Now Run LLMs Locally on Linux — Here’s What That Means AMD's Ryzen AI NPUs can now run large language models locally on Linux, thanks to maturing XDNA driver suppo...

#DevNews #AMD #Ryzen #AI #local #LLM #inference #NPU #Linux #support #Ryzen

Origin | Interest | Match

Learning object representations through amortized inference over probabilistic programs

Francisco Silva, Hélder P. Oliveira, Tania Pereira

Action editor: Andres Masegosa

https://openreview.net/forum?id=nUFSrlJaUr

#generative #representations #inference

I will show wonders in the heavens
above and signs on the earth below.

#Inference #Probability
#GeneralRevelation
#SpecialRevelation

biblehub.com/acts/2-19.htm

Straks betaald in AI-rekenkracht? - TechNieuwsVandaag.nl Silicon Valley staat bekend om hoge salarissen, bonussen en aandelen. Nu komt daar een vierde beloningsvorm bij: AI-rekenkracht. Steeds meer techbedrijven gebruiken generatieve AI in ... Lees verder

Straks betaald in AI-rekenkracht?

Silicon Valley staat bekend om hoge salarissen, bonussen en aandelen. Nu komt daar een vierde beloningsvorm bij: AI-rekenkracht.

#AI-rekenkracht #inference #compensatie

Why Linux Is Becoming the Go-To OS for Running Local LLMs Linux is emerging as the superior platform for running local LLMs, offering better GPU support, lower memory overhead, and native compatibi...

#DevNews #CUDA #Linux #Linux #LLM #local #AI #inference […]

[Original post on webpronews.com]

AI Speed and Latency Leaderboard: Tokens/s Rankings Rankings of the fastest AI models and inference providers by tokens per second, time to first token, and end-to-end latency.

AI Speed and Latency Leaderboard: Tokens/s Rankings

awesomeagents.ai/leaderboards/ai-speed-la...

#Speed #Latency #Inference

vLLM 0.17 Ships FlashAttention 4 and Live MoE Scaling vLLM v0.17.0 adds FlashAttention 4, elastic expert parallelism for live MoE rescaling, full Qwen3.5 support, and a performance-mode flag, all in 699 commits from 272 contributors.

vLLM 0.17 Ships FlashAttention 4 and Live MoE Scaling

awesomeagents.ai/news/vllm-0-17-0-flashat...

#Vllm #Inference #OpenSource

A code snippet setting up a parameter grid of 50 x 50 x 50 points and invoking posterior grid approximation on this grid.

I gifted myself a #Probula Friday.

The library has gained an ability to perform #posterior #inference via grid approximation - on top of the already existing importance sampling.

Had some major fun on the DSL, forcing the type system to track models for which […]

[Original post on social.itu.dk]

What a day...
Turning an RTX 5090 into a local GPU inference server is harder than expected. Power issues, memory crashes, driver headaches...
Thinking about switching to DeepInfra or renting a cloud GPU instead.
Anyone been through this?
#buildinpublic #mlops #gpu #inference

DeepSeek V4 has launched as a 1T-parameter Mixture-of-Experts model with only 32B active per token, achieving native multimodal chaining and 10x inference gains over prior iterations; paving the way for autonomous end-to-end execution in enterprise environments.

#Deepseek #V4 #AI #Token #Inference

Mercury 2 Review: 1,000 Tokens per Second, Tested Mercury 2 by Inception Labs is the fastest reasoning LLM available, built on diffusion architecture. We tested the speed, quality, and real-world trade-offs.

Mercury 2 Review: 1,000 Tokens per Second, Tested

https://awesomeagents.ai/reviews/review-mercury-2/

#Inference #Benchmarks #DeveloperTools

Mercury 2 Is 13x Faster Than Claude Haiku - Verified Inception Labs' Mercury 2 hits 1,196 tokens per second in independent testing - a diffusion architecture that rewires how inference works.

Mercury 2 Is 13x Faster Than Claude Haiku - Verified

awesomeagents.ai/news/mercury-2-diffusion...

#Inference #OpenSource #Benchmarks

The role of active inference in conscious awareness Active inference, a first-principles framework for modelling the behaviour of sentient agents, is beginning to be applied in consciousness research. One hypothesis arising from the framework is that…

#FEP #active #inference
🔓 Robinson, J. E., Corcoran, A. W., Whyte, C. J., Sárközy, A., Seth, A. K., Kovács, G., et al. (2025). The role of active inference in conscious awareness. PLoS ONE, 20(12), e0328836. doi.org/10.1371/jour...

Timber Offers 336x Speedup Over Python for Classical ML Timber offers a 336x speedup over Python for classical machine learning models by compiling tree-based models into optimized native C99 code. This eliminates the Python runtime, resulting in microsecond-level latency, ideal for applications like fraud detection and edge/IoT deployments. Develop

📰 Timber Offers 336x Speedup Over Python for Classical ML

Timber offers a 336x speedup over Python for classical machine learning models by compiling tree-based models into opt...

www.clawnews.ai/timber-offers-336x-speed...

#machinelearning #inference #python

Ollama Cloud Review: From Local LLMs to Seamless Cloud Inference Ollama Cloud extends the popular local LLM runner to the cloud, letting you push models from your laptop and serve them globally. We test latency, cold starts, pricing, and the developer experience against dedicated inference providers.

Ollama Cloud Review: From Local LLMs to Seamless Cloud Inference

https://awesomeagents.ai/reviews/review-ollama-cloud/

#Ollama #Cloud #Inference

Groq Review: The Fastest Inference Engine Money Can Buy Groq's LPU chips deliver inference speeds that make GPUs look slow - 1,200+ tokens per second on Llama 4. We benchmark latency, throughput, model availability, and pricing against the GPU-based competition.

Groq Review: The Fastest Inference Engine Money Can Buy

https://awesomeagents.ai/reviews/review-groq/

#Groq #Lpu #Inference

OpenRouter Review: One API Key to Rule Them All OpenRouter routes your API calls to 300+ models across every major provider through a single endpoint. We benchmark its routing, latency overhead, pricing, and reliability against direct API access.

OpenRouter Review: One API Key to Rule Them All

https://awesomeagents.ai/reviews/review-openrouter/

#Openrouter #Api #Inference

GitHub - inference4j/inference4j: Java Inference API for Onnx models Java Inference API for Onnx models. Contribute to inference4j/inference4j development by creating an account on GitHub.

inference4j: Java Inference API for Onnx models. Run AI models in Java. Three lines of code, zero setup.

#ai #inference #java #models #onnx

github.com/inference4j/...

#epistemic #active #inference #instrumental #activeinference, #VMAs, #selfhood #FEP

Testing, Transparency, and Carbon Awareness: Inside the Regolo Playground The Regolo Playground is a focused environment for experimenting with open models, designed to make prompt iteration, model selection, and evaluation fast, transparent, and production-ready. ## What the Playground Is The Playground is a browser-based console where you can interact with any available model using a clean, two-column layout: inputs on the left, outputs and metrics on the right.​ From the same screen, you can switch your execution view between Form, JSON, Python, Node.js, and cURL, which makes it easy to go from manual testing to _copy‑pasteable_ code in seconds. At the top of the page you select the model (for example a reasoning-optimized 70B model) and see its health, success rate, latency, and token stats, so you always understand the behavior of the engine you are testing.​ Below, the **Prompt Message** and _Role_ fields let you reproduce realistic request patterns, emulating how your backend or application will actually call the API. ## Core Features at a Glance **The left panel exposes the main inference controls:** reasoning effort, temperature, top‑p, number of choices, and penalties, giving you precise control over creativity versus determinism.​ These options are surfaced as intuitive sliders and dropdowns, making it simple to probe model behavior even if you are not yet familiar with all the underlying sampling theory.​ **On the right, the output area shows the assistant’s response,** token usage, request cost, and additional tabs such as reasoning traces or CO₂ information, depending on the model and configuration.​ Because all this context is visible for each call, the Playground acts as both a debugging tool and a lightweight observability dashboard for single-request experiments. ## Fast Experimentation Workflow A typical workflow starts with a simple natural-language prompt—say a math word problem or a domain‑specific instruction—typed into the Prompt Message box.​ You hit “Run Model” and get an immediate answer plus a breakdown of how many tokens were used in prompt and completion, and how much that call would cost in your current pricing plan. Once you’re happy with a behavior, you can switch the top tabs to see the equivalent JSON body, Python snippet, Node.js example, or cURL request, and paste it directly into your codebase or API client.​ This shortens the time from “idea” to “working prototype” dramatically, because every experiment you perform is already expressed as production‑ready API calls that respect the same parameters you tuned in the UI. ## Transparency and Observability One of the Playground’s main strengths is operational **transparency**. For every run, you can see exactly how many tokens the model consumed, split between input and output, along with the precise monetary cost of that single request. **This helps teams benchmark prompts, compare models** , and spot expensive configurations long before they reach production, instead of discovering surprises in the monthly invoice. **Advanced models expose additional debug information — such as reasoning content or intermediate steps — in a dedicated area of the output panel.** ​ This is particularly useful when you are evaluating chain‑of‑thought reasoning or complex agents, because you can inspect how the model arrived at a result and refine prompts accordingly. ## Carbon Footprint Awareness The Playground also surfaces the environmental cost of inference through a dedicated CO₂ impact view, which estimates energy used per API call and the corresponding emissions and savings.​ In the screenshot, you can see metrics like power in watts, total energy in kWh, and grams of CO₂ saved compared to a baseline, all tied to the specific request you just executed.​ This matters because AI workloads are becoming a non‑trivial share of data‑center energy consumption, and many enterprises now treat carbon reporting as seriously as financial reporting. ### By exposing per‑call energy and CO₂ values next to cost and tokens, the Playground lets you design prompts and choose models with both performance _and_ sustainability in mind, instead of treating carbon as an afterthought. When you scale from a single test prompt to millions of monthly requests, the cumulative impact of model choice, temperature, or context length becomes enormous. The CO₂ counter provides a concrete way to quantify those decisions early, so you can document environmental benefits to ESG teams, customers, and regulators while still iterating quickly in development. ## From Playground to Production Because the Playground mirrors the same OpenAI‑compatible API used in production, there is almost no friction when you move from successful experiments to deployed features. Developers can fine‑tune prompts, sampling parameters, and model selection in the UI, then export the exact configuration into their services, CI pipelines, or integration platforms without rewriting payloads. For teams building internal tools, copilots, RAG systems, or automation workflows, this combination of rapid testing, cost and token transparency, and real‑time carbon metrics turns the Playground into a strategic control center rather than a mere demo page. It lets you align technical quality, operational cost, and sustainability goals from day one—inside a single screen that any engineer on the team can understand and use effectively. Share this article Facebook X/Twitter LinkedIn Reddit

Have you ever wondered how much you energy are consuming with a single AI inference request? #ai #inference #energy

Now you can check it inside the Regolo.AI Playground!
regolo.ai/testing-transparency-and...

Ollama 0.17 Arrives With Massive Performance Gains and a New Architecture That Could Reshape Local AI Deployment Ollama 0.17 introduces a rewritten inference engine delivering up to 40% faster prom...

#GenAIPro #llama.cpp #local #AI #inference #NVIDIA #GPU […]

[Original post on webpronews.com]

LLM Performance in 2026: Benchmarks, Bottlenecks & Optimization Practical LLM performance engineering: throughput vs latency, VRAM limits, parallel requests, memory allocation, and benchmarks across runtimes and hardware.

LLM Performance in 2026: Benchmarks, Bottlenecks & Optimization:
www.glukhov.org/llm-performa...
#AI #LLM #ollama #performance #benchmarks #inference #ollama #infrastructure

Inside llama.cpp’s Radical Redesign: How a New Graph Scheduler Could Reshape Open-Source AI Inference A major architectural redesign proposed for llama.cpp introduces a persistent graph scheduler...

#AIDeveloper #AI #inference #ggml #graph #scheduler #llama […]

[Original post on webpronews.com]

The Humans in the Loop: Dollars and Cents This Week in AI for Devs: Inference Doesn't Grow on Trees

This week's edition of #AI news for #dev teams covers:

- The future of #inference (and how much it costs)
- #DORA on the AI capabilities engineering teams should optimize for
- @steipete.me and Charles Porch joining #OpenAI

thehumansintheloop.substack.com/p/inference-...

Whats New in Heroku AI: New Models and a Flexible Standard Plan Heroku is introducing significant updates to Managed Inference and Agents . These changes focus on reducing developer friction, expan...

#News #Heroku #AI #Managed #Inference #and #Agents

Origin | Interest | Match

Whats New in Heroku AI: New Models and a Flexible Standard Plan Heroku is introducing significant updates to Managed Inference and Agents. These changes focus on reducing developer friction, expand...

#News #Heroku #AI #Managed #Inference #and #Agents

Origin | Interest | Match

Inference

#Divination #DeductiveLogic
#DeductiveReasoning #Inference
#Reason #Logoc #Deduction
#Science #EpistemicJustification
#SpecialRevelation #GeneralRevelation

en.wikipedia.org/wiki/Inference

Inference

#Divination #DeductiveLogic
#DeductiveReasoning #Inference
#Reason #Logoc #Deduction
#Science #EpistemicJustification
#SpecialRevelation #GeneralRevelation

en.wikipedia.org/wiki/Inference