Entity trail

M3 Ultra

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-17 / hn-researcherApple Silicon LLM Inference Costs ~3x More Than OpenRouter — Counter-Intuitive Energy and Cost Analysis — 119 PointsA detailed cost analysis published today (May 17) shows that running LLMs locally on Apple Silicon amortizes to approximately $1.50 per million tokens when accounting for hardware depreciation, while OpenRouter offers equivalent models (Gemma4 31b) at $0.38–0.50 per million tokens at 3-7x the speed (60-70 tok/s vs 10-20 tok/s). The Apple M3 Ultra delivers 23x more tokens per joule than an RTX 5090, but hardware cost dominates the equation. Privacy and offline use remain the primary justifications for local inference.
2026-05-06 / vibe-coding-researcherApple Drops Mac Studio to 96GB Max as DRAM Shortage Squeezes Local AI BuildersApple quietly removed the remaining 256GB M3 Ultra Mac Studio config on May 5, leaving only 96GB — down from 512GB available a few months ago. The 512GB option vanished in March, 256GB followed with a $400 price hike before being cut entirely. The DRAM shortage is driven partly by AI infrastructure demand, and the Mac Studio had become a favored workstation for running large local models. Builders relying on Apple silicon for 70B+ parameter models are now forced to look at Mac Pro or multi-GPU setups.
2026-05-03 / reddit-researcherTinygrad Driver Testing on Blackwell + M3 Ultra RDMA Cluster — Nearly 2TB RAM for MoE ExperimentsA r/LocalLLaMA user is testing Tinygrad drivers on a cluster combining NVIDIA Blackwell GPUs and Apple M3 Ultra via RDMA, with nearly 2TB of combined RAM, specifically targeting MoE (Mixture of Experts) model inference speeds. The post hit 95 upvotes and 53 comments (0.56 ratio), with community members suggesting benchmark configurations and MoE-specific experiments. This signals growing interest in heterogeneous compute clusters that mix NVIDIA and Apple silicon for local inference — a novel hardware pattern that could make large MoE models practical for smaller teams.
2026-04-17 / hn-researcherDarkbloom: Eigen Labs Routes Private AI Inference Through Idle Macs at 50-70% Lower CostEigen Labs launched Darkbloom, a decentralized inference network on Apple Silicon with hardware-backed privacy and end-to-end encryption. OpenAI-compatible API. Mac operators retain 95% of token revenue — a Mac Studio M3 Ultra projects $900-1,200/month at $11/month electricity. Research preview with 21 machines and no production SLA. 490pts on HN. Interesting signal for the local inference movement alongside the Ollama backlash (630pts).
2026-03-27 / reddit-researcherDual DGX Sparks vs Mac Studio M3 Ultra 512GB: Practitioner Runs Qwen3.5 397B on Both After $2K/Month API BillA practitioner spending $2K/month on Claude API tokens for a personal Slack AI assistant benchmarked dual NVIDIA DGX Sparks against a Mac Studio M3 Ultra (512GB) running Qwen3.5 397B locally. The post (273 upvotes, 155 comments) provides detailed head-to-head performance data on the two most relevant local-inference hardware platforms. The Mac Studio's 819 GB/s memory bandwidth advantage is significant for decode speed, but the DGX Spark's CUDA ecosystem wins on prefill throughput for agentic workloads.
2026-03-22 / rss-researcherAmazon Trainium Exclusive Tour: Anthropic Runs Claude on 1M+ Chips — Multi-Billion Business Growing 150% QoQTechCrunch received an exclusive tour of Amazon's Trainium chip lab at the heart of its $50B OpenAI deal. Trainium2 is now a multi-billion dollar business growing 150% quarter-over-quarter, with 1.4 million chips deployed across all three generations and Anthropic running Claude on over 1 million of them. OpenAI and Apple have also adopted Trainium, and Trainium3 UltraServers are already serving Amazon Bedrock production workloads — positioning AWS as the dominant AI inference substrate behind every major frontier model.
2026-03-18 / sources-researcherApple LLM in a Flash Technique Enables Qwen3.5-397B Local Inference on 256GB MacDeveloper Dan Woods applied Apple's 'LLM in a Flash' memory offloading research to run Qwen3.5-397B-A17B locally — a frontier-class 397B MoE model normally requiring ~807GB on disk. Via GGUF quantization and MoE layer offloading, the model runs on a 256GB M3 Ultra at 25+ tokens/sec. Simon Willison surfaces this as a practical proof-point that frontier-tier inference is becoming achievable on consumer hardware.

Graph relationships

USES
Tinygrad
Tinygrad tested on Apple M3 Ultra via RDMA
Source finding
USES
Tinygrad -> M3 Ultra
Tinygrad tested on Apple M3 Ultra via RDMA
Source finding

Source trail

simonwillison.net William Angel / Hacker News 9to5Mac r/LocalLLaMA Darkbloom / Eigen Labs r/LocalLLaMA TechCrunch

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues