Betelgeuse runs Qwen 3.8 Flash Next at 24 tok/s on 2x8GB DDR4 plus RTX 3060
Read original source →reddit.com
The bel-cli/bel-server project hit 24 tok/s on IQ4_XS with 0 dropped experts and 100% byte-exact output, versus 14 tok/s on a single 16GB DDR4 stick. An earlier run reached 22 tok/s sustained (48 tok/s spikes) on IQ2_XS.
2 sources
More stories today
Short film "Catch Me" made by one creative in days using Runway
Runway·1 hour ago
Qwen head says open-weight releases will continue
A statement attributed to Qwen head Liu Dayi Heng denies rumors that Qwen4 would be the last open-weight series, with future releases going API-only. The post says the statement contradicts those rumors and affirms Qwen's open-source stance.
r/LocalLLaMA·1 hour ago
42AI exits stealth with $62.5M for behavioral world model
Israeli startup 42AI raised $62.5M in seed funding for a "behavioral world model" it says predicts how individuals will decide in new situations. It is running the system at a handful of financial brokerages to forecast trader reactions to volatility.
TechCrunch·1 hour ago
Hume AI ships audio-native decision model for voice agents
Hasan Toor·2 hours ago
Microsoft publishes Humanist AI Code of Conduct
Mustafa Suleyman·2 hours agoAnthropic says AI-driven AI progress may soon hit its acceleration threshold
Kimmonismus·2 hours ago
Figure's newly manufactured F.03 robots leave BotQ facility
Brett Adcock·2 hours ago
NVIDIA Workload Map gives Kubernetes a shared workload schema
NVIDIA Workload Map, part of the DSX AI Factory Platform, describes a distributed JobSet workload once so schedulers, controllers and dashboards can reuse it instead of writing a per-CRD adapter for each framework. The Run:ai team built it after hitting the same parsing problem for every new custom resource definition.
NVIDIA Developer Blog·2 hours ago
