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local AI deployment
Trends
- 1Qwen 3.8 Flash Next 125B runs at 100 tokens/s on RTX 4090●Run Qwen 3.8 Flash Next (125B) on consumer hardware (RTX 4090) at 100T/s
A new open-source project called Strata claims it can run Alibaba's Qwen 3.8 Flash Next, a 125-billion-parameter model, on a single consumer RTX 4090 graphics card at roughly 100 tokens per second. If the benchmarks hold up, it would make a frontier-class large language model practical on gaming-grade hardware without a data centre, which is why developers are scrutinising the code and debating how the performance is achieved.
- 2Open-Source Edge Inference Engine Runs Large AI Models on Robots 10.7x Faster▼10.7x Faster: This Open-Source Edge-Side Inference Engine Enables Robot Bodies to Run Large Models Without Lag
A new open-source edge-side inference engine claims a 10.7x speedup, allowing robot hardware to run large AI models locally without lag. The technology targets real-time on-device inference for robotics, reducing reliance on cloud computing. Discussion is centered on its performance gains and what faster local inference could mean for embodied AI and robot deployments.
- 3McDonald's uses AI to estimate customer willingness to pay▼McDonalds has AI estimate "customer willingness to pay" at each restaurant
McDonald's is deploying artificial intelligence to estimate how much customers at individual restaurants are willing to pay, a Reuters report says, part of a wider push to use AI-driven dynamic pricing on menu items like the Big Mac. Critics see it as algorithmic price discrimination aimed at squeezing customers, while the company frames it as smarter pricing tied to local demand.
- 4AI decision models: what they are and how to run them locally▼AI decision models, what they are and which you can run locally
A new explainer outlines what AI decision models are, breaking down the systems that make automated choices, and details which of them can be run locally on personal hardware rather than in the cloud. The piece walks through the main categories of decision-making models and offers practical guidance for users wanting more privacy and control by keeping their AI tools on their own machines.
- 5
The Register reports on a new open source tool that distills Jev, making it possible to run it on local hardware rather than in the cloud. Distillation shrinks a model so it can run on ordinary machines, lowering cost and keeping data private. The piece describes the tool and what it means for developers wanting offline use.
- 6AWS releases open source tool to control AI agents▼AWS offers local, open source leash for agent harnesses
AWS has launched a locally run, open source tool for keeping tabs on AI agent harnesses, the software frameworks that let autonomous AI systems take actions. The offering gives developers a way to monitor and constrain agent behaviour on their own infrastructure rather than relying on hosted services. It reflects growing demand for guardrails as companies deploy agentic AI in production.
- 7Federal judge calls Flock surveillance system indiscriminate mass surveillance●Privacy & security, Sun, Oct 4: • Federal judge calls Flock 'indiscriminate mass surveillance' https:// techcrunch.com/2
A federal judge has sharply criticized Flock, the automated license plate reader company, describing its camera network as 'indiscriminate mass surveillance.' The ruling adds to mounting legal scrutiny of Flock's partnerships with local police departments across the United States. Privacy advocates are amplifying the decision alongside other security concerns, including Anthropic asking Claude users to share voice recordings for AI model training.
Repos
- openclaw/openclaw The AI that really does things. Any OS. Any Platform. The lobster way. 🦞