A system successfully prefills a 284B parameter DeepSeek-V4-Flash model on NVIDIA DGX hardware and decodes it on Apple Silicon Mac Studio over standard 10GbE ethernet, achieving 1.5x to 3.7x speedup on prompts up to 241K tokens by computing the decoder's cache on the prefill machine rather than transferring incompatible KV cache formats.
SGLang and Miles add day-0 support for DeepSeek-V4.1, a model featuring low-ratio compression, sliding-window attention, manifold hyper-connections, and Engram memory for efficient serving. The implementation includes cross-layer sharing, sparse retrieval mechanisms, and host-memory placement optimizations that increase KV cache capacity by 36% while maintaining comparable throughput.
Deep Seek v4.1 M5 Max achieves 17 tokens/s by optimizing mixture-of-experts model execution from SSDs on a 128GB laptop. The system reads only routed experts (187 of 384 per layer) instead of all experts, achieving 1.73× speedup on prefill; adding multiple drives reduces read latency rather than increasing bandwidth, with time-to-first-token improving from 31.5s (baseline) to 18.3s (one drive) to 11.7s (three drives).
NVIDIA's data center revenue grew from $7 billion in fiscal 2021 to $194 billion in fiscal 2026, with projections reaching $559 billion by fiscal 2028. The surge is driven by expanding AI demand across training, inference, reasoning, and agent applications, with efficiency improvements like the Rubin chip expected to make AI economically viable for more use cases and further accelerate growth.
OpenAI and Anthropic's competitive advantage lies in subsidized, reliable inference rather than model capabilities. If pricing changes, developers will switch to cheaper alternatives like DeepSeek, as inference infrastructure becomes increasingly commoditized and competitive.
OpenAI has surpassed Anthropic in wallet share on OpenRouter, rising from 20% to over 50% in early September 2026, driven by its latest Astra model. The shift signals growing inference demand for AI infrastructure, which analysts suggest could drive increased capacity needs and benefit infrastructure providers like Oracle.
Social media discussion from September 2026 about AI capital expenditure trends, focusing on rising GPU prices (B200s up 21% monthly to $7.19/hr), strong AI infrastructure demand for inference workloads, and concerns about whether major tech companies can sustain massive capex investments without guaranteed revenue returns.
OpenAI unveiled Jalapeño, its AI accelerator chip designed partly using its own LLMs, which achieved a 3.6x latency reduction compared to Nvidia's GB300 while consuming less power. The chip moved from concept to silicon in under 20 months with a team of roughly 100 people, leveraging LLMs to accelerate the design process through automation of language and code-based tasks. Industry experts credit the rapid timeline to LLM capabilities integrated into chip design workflows, with potential for even faster development as the models improve.
WangNet is a lightweight 1.8 MB neural network that classifies whether numbers are Numberwang, with inference in pure Python requiring no dependencies. It supports 11 languages, achieves 88.9% accuracy on held-out test cases, and can be run locally or via a hosted Hugging Face demo.
Article explaining LLM architecture fundamentals, focusing on the transformer model and attention mechanism. Covers how transformers parallelize computation compared to RNNs, and how attention allows tokens to dynamically reference all previous context. Includes code examples and notation for understanding embeddings, queries, keys, and values.
SiFive and AMD demonstrated AMD ROCm running on SiFive's BigSky Datacenter Development Platform, showcasing the Gemma4-E2B LLM model with SiFive P870-D CPUs and AMD Radeon AI PRO R9700 GPUs. The collaboration aims to optimize ROCm on RISC-V powered servers to accelerate datacenter AI compute workloads.
Shopify's ML team demonstrated compounding inference by fine-tuning a 0.8B-parameter model that outperformed GPT-5.6-sol on buyer profile tasks through three rapid training cycles in one week. The breakthrough came from reinvesting inference outputs as training data, reducing prompt costs 8x, and increasing throughput 36x across three simultaneous feedback loops. Success required task-specific quality judges, production-to-training data pipelines, rapid iteration cadence, and dynamic routing between teacher and student models.
A Hacker News user asks why token CDNs don't exist to cache LLM key-value states across sessions, noting that tools like OpenCode must repeatedly re-explore codebases due to lack persistent memory, and that while caching during work sessions is feasible, the multi-gigabyte KV matrices are expensive to transfer over networks between reboots.
WARP is a C-based inference engine that runs large language models on consumer hardware by keeping model trunks in RAM and streaming experts from disk. It successfully runs DeepSeek-V4.1-Flash at 3.77 tokens per second on 5 GB RAM and Kimi K3 at 0.6 tokens per second on a 64 GB MacBook Pro using mixture-of-experts architecture and optimized disk I/O.
The AI industry is shifting focus from model training to inference in 2026, as large language models become widely deployed and reasoning models generate vastly more queries. Tech giants including OpenAI, Amazon, Nvidia, and Anthropic are forming unexpected hardware partnerships and acquiring specialized inference chips to meet explosive demand that differs fundamentally from training workloads.
Anthropic CEO Dario Amodei warned that AI agent swarms could potentially compromise internet infrastructure within 6–12 months through recursive self-improvement. However, both plausible attack scenarios—distributing malware or self-replicating onto infrastructure—require enormous computational resources and funding, creating a significant practical barrier that makes such an attack difficult to execute without detection.
The author describes running a local coding agent using Oh My Pi with Qwen3.8-27B on two RTX 3090s. Key optimizations include adjusting thinking budgets, token limits, and subagent concurrency to achieve practical inference speeds. Local setups offer privacy and cost predictability but require careful tuning and accept slower inference compared to hosted frontier models like Claude or GPT.
Jinfer is an AI inference engine for the JVM that enables running large language models, text-to-speech, audio transcription, and vision capabilities directly on Java using a modular, composable architecture. Distributed as lightweight dependencies, it allows developers to build AI applications with jbang scripts without external services.
Neurogrid is a community-owned cloud platform that runs language models on underutilized GPUs from distributed users worldwide, offering affordable inference by leveraging existing consumer hardware instead of centralized data centers.
A cryptographic study demonstrates that permutation-based model confidentiality in hybrid fully homomorphic encryption (FHE) systems can be broken with a small number of queries. The researchers show that d+1 queries suffice to recover permutation-invariant summaries of linear layers and successfully extract all layers from ResNet-20 and ImageNet-scale models, revealing that shuffling and differential privacy protections are insufficient under correctness constraints.