Xiaomi open-sourced MiMo-V2.5-Pro with 1.02T parameters, 42B active parameters, a 1M context window, and an MIT license; the author ran 125 Claude Code sessions through the API, spending $70.12 for 387,380,436 tokens with a 96.3% cache hit rate.

Trump called Jensen Huang on Tuesday morning to invite him to join the China trip; the White House’s Monday list of 16 CEOs did not include him, while Nvidia is still seeking approval to sell AI chips to China.

DeepSeek optimized its latest model for inference on Huawei chips for the first time, while two semiconductor sources said training still relies on Nvidia chips; Huawei says it plans to release a training chip this year, but matching current Nvidia performance will take another year.

maddiedreese ran Andrej Karpathy’s TinyStories-260K on a stock Game Boy Color with INT8 weights, fixed-point math, an MBC5 ROM, bank-switched cartridge storage, and KV cache in cartridge SRAM; the demo uses no phone, PC, Wi‑Fi, link cable, or cloud inference, but output is extremely slow and gibberish.

CME Group and Silicon Data plan to create a futures market for computing power; the RSS snippet says Bloomberg Tech discusses the rationale and mechanics, but the post does not disclose contract specifications, launch timing, or pricing methodology.

Claude Opus 4.7 Fast Mode is now available as a research preview in the API and Claude Code. The post does not disclose model parameters, pricing, rate limits, or a general availability date.

Cactus open-sourced Needle, a 26M-parameter tool-calling model that reaches 6,000 tok/s prefill and 1,200 tok/s decode on consumer devices, with MIT-licensed weights released on Hugging Face.

Cactus Compute open-sourced Needle, a 26M-parameter tool-calling model that reaches 6,000 tok/s prefill and 1,200 tok/s decode on consumer devices, using an attention-and-gating architecture with no MLPs.

Google and SpaceX are discussing orbital data centers for AI compute, while the post says current space costs remain higher than ground infrastructure; the RSS snippet does not disclose cost gaps, deployment scale, or a timeline.

CME plans to launch futures contracts tied to GPU rental prices, allowing traders and companies to bet on or hedge future costs; the RSS snippet does not disclose contract specifications, launch timing, or the reference index.

Reddit user grumd runs Qwen2.5-Coder-7B Q6 for autocomplete and Qwen3.6-35B-A3B Q8 for agentic coding on one RTX 5080 with RAM offloading; the post reports about 145k context, 56GB RAM used with other apps open, and Qwen3.6-35B-A3B speed of tg128 at 35.29 tokens/s.

ByteDance open-sourced DreamLite, a 0.39B-parameter unified diffusion model that generates or edits a 1024×1024 image on an iPhone 17 Pro in about 3 seconds, using 4-step DMD2 distillation and on-device offline inference without cloud dependency.

OpenAI’s Parameter Golf brought together over 1,000 participants and more than 2,000 submissions to test AI-assisted machine learning research, coding agents, model quantization, and model design under strict parameter constraints.

The author proposes prompt caching for RL training. On Qwen3.5-4B, it reports a 7.5x speedup with 16k-token prompts and 64-token outputs, and the G=8 example with 1000-token prompts and 100-token responses reduces 8800 processed tokens to 1800 unique tokens.

Reddit user APFrisco ran the 1T-parameter Kimi K2.5 Q2_K_XL quant locally at about 4 tokens/s using 768GB Intel Optane PMem, 192GB DDR4 ECC DRAM, and a 12GB RTX 3060 with llama.cpp hybrid GPU/CPU inference.

ExLlamaV3 added DFlash in v0.0.31, raising Coding throughput from 59.21 t/s to 177.67 t/s; v0.0.32 optimized five models, with Trinity-Nano gaining 72.4% on 6000 Pro², while v0.0.33 adds DFlash model quantization plus bug fixes and efficiency work.

PRISM raises LLaDA-8B-Instruct on GSM8K from 67.58% to 85.30% by combining hierarchical trajectory search, partial remasking, and self-verified feedback, reducing dLLM test-time scaling cost from O(NT) toward O(N+KT) under a final candidate width K.

Cerebras raised its IPO price range to $150-$160 per share, targeting about a $35 billion valuation at the top end, after OpenAI signed a 750-megawatt AI compute purchase agreement with deliveries through 2028.

SpaceX filed two SpaceXAI trademark applications covering satellite-based data centers, orbital computing, AI SaaS, cloud storage, telecom hardware, and social networking; the post says xAI became a SpaceX subsidiary through an all-stock deal and cites a $250 billion xAI valuation.

Cerebras received more than 20 times oversubscription for its IPO and plans to raise the share count from 28 million to 30 million while increasing the price range to $150-$160.

A five-week experiment tested about 1,400 daily work tasks, where local 35B models such as Qwen 3.6 35B handled about 50% and averaged 2.8-second responses, 2.1 times faster than Claude Opus 4.5, while the cloud model still led complex reasoning by about 20%.

A Reddit LocalLLaMA user ran 300+ tests on Qwen 3.6 27B MTP quants, finding coding draft acceptance at 79-89% and F16 coding speed up 171%, while Q4_K_M creative writing slowed down 9%.

Reddit user fairydreaming ran DeepSeek V4 Pro Q4_K_M with a modified llama.cpp CUDA repo on one RTX PRO 6000 Blackwell Max-Q workstation GPU, using an 859GB model file; the shared log reports a 1M context window and 8.6 tokens per second generation speed.

NVIDIA AI released Star Elastic, a single checkpoint that can zero-shot slice 30B, 23B, and 12B reasoning models in BF16, FP8, and NVFP4; when the 23B submodel handles thinking and the 30B model handles final answers, reported accuracy rises 16% and latency drops 1.9× on AIME-2025, GPQA, LiveCodeBench v5, and MMLU-Pro.

Anbeeld released BeeLlama.cpp, a llama.cpp fork that runs Qwen 3.6 27B Q5 with 200k context and vision on a single RTX 3090 or 4090; the title claims 2–3x faster than baseline and a 135 tps peak.

Antirez open-sourced ds4, a native inference engine for DeepSeek V4 Flash that uses a few thousand lines of C to run a 1M-context model on a 128GB MacBook Pro at a reported 27 tok/s.

Reddit user janvitos ran Qwen3.6-35B-A3B-MTP-GGUF with a llama.cpp MTP PR on an RTX 4070 Super. The posted benchmark shows 69.2-81.9 tok/s, 0.694-0.947 draft acceptance, 131072 context, and a -fitt 1536 setting that reserves 1536 MB for the draft model and KV cache.

Baidu released ERNIE 5.1 with total parameters reduced to about one third of the original scale, active parameters to about one half, and pretraining cost to about 6% of same-scale models; the model is available on the ERNIE platform and Baidu AI Studio.

RadixArk announced a $100 million seed round on May 5 at a $400 million post-money valuation, led by Accel and co-led by Spark Capital, with participation from NVentures, AMD, MediaTek, Databricks, and other investors tied to AI infrastructure.

DeepSeek is raising up to $7 billion at a $50 billion valuation, with founder Liang Wenfeng personally contributing $3 billion, or 40% of the round, while the company says the funding will target large-scale compute, V4.1 model releases, enterprise products, and a path toward positive revenue.

indrasmirror ran Qwen3.6-27B-Heretic-v2 on a single RTX 4090 with 262K context, TBQ4_0 KV cache, and MTP draft 3, improving throughput from about 43 t/s to 80-87 t/s with roughly 73% MTP draft acceptance.

The Verge tracks AI data center disputes with specific updates: 43% of Americans blame data centers for rising power bills, a 40,000-acre Utah project won approval despite local opposition, and Anthropic says it will invest $50 billion in US AI data centers.

Anthropic signed a $1.8 billion computing deal with Akamai to meet rising demand for its AI software; the post does not disclose capacity, contract duration, or deployment regions.

AllenAI introduced EMO, a mixture-of-experts model with 14B total parameters and 1B active parameters, trained on 1 trillion tokens and able to use only 12.5% of its experts for specific tasks while retaining near-full-model performance.

chain-77 benchmarked Gemma 4 26B with vLLM 0.19.2rc1, and DFlash raised output throughput on one RTX 5090 from 228 tok/s to 578 tok/s under 256 input tokens, 1024 output tokens, concurrency 1, and num_speculative_tokens=13.

RadixArk announced a $100 million seed round on May 5 at a $400 million post-money valuation, while its SGLang inference project has 27K+ GitHub stars and deployments across 400K+ GPUs.

UC Santa Barbara and Apple researchers introduced LenVM, which models remaining generation length as a token-level value function; Qwen2.5-3B with a 1.5B LenVM scored 62.6 on LIFEBench length control, above GPT-5.4 at 37.4 and Claude-Opus-4-6 at 35.5.

HIT Shenzhen, Huawei, and Shenzhen Hetao College proposed Dynamic-dLLM, a training-free dLLM acceleration framework that raises LLaDA-8B-Instruct throughput on GSM8k from 8.32 TPS to 37.29 TPS with almost no accuracy loss.

Big Tech is spending $725 billion on AI infrastructure, and the title says free cash flow has fallen to a decade low; the RSS snippet says Silicon Valley giants shifted from asset-light cash generators to infrastructure investors, but it does not disclose the company list, time period, or accounting basis.

Doug_Bitterbot says TOPAS scored 11.67% on ARC-AGI-2 using one RTX 4090 after about 14 days of training. The 100M-parameter checkpoint hit 36% locally, but recursive TTT caused null outputs on nearly half of Kaggle puzzles. The key detail is time management: the author expects 20% after threshold tuning and 3-5 more weeks of training.