Deployment & engineering

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.