A wave of Chinese models—DeepSeek V4 Flash, Ling-3.0, GLM-5.3—are released with massive expert counts and open weights, democratizing capabilities. Their compatibility with local inference engines is accelerating on-premise adoption.
A user’s updated local benchmark places the MXFP4-quantized DeepSeek V4 Flash 0731 at the top for efficiency and quality, delivering 1,000 tokens/sec prefill and 90 tokens/sec generation. The result shines a spotlight on low-precision quantization an...
InclusionAI has released Ling-3.0-flash weights on Hugging Face. The model packs 127.5 billion parameters with 512 experts, 8 active per token. The real story is the official FP8 version: 128GB, enough for a single unified-memory system or a multi-GP...
InclusionAI released Ling-3.0-flash, an open-weight MoE with 124 billion total parameters but only 5 billion active per token. Announced before the Kimi K3 and DeepSeek-V4-Flash wave, its sizing could carve a niche in on-premise deployment, where eff...
A commit in Z.AI's Java SDK hints at a new GLM-5.3 model. It's a signal that precedes the update of an LLM family already popular in Chinese on-premise scenarios, rekindling the debate on data sovereignty and competition with Western cloud vendors.
A Reddit PSA warns DeepSeek-V4-Flash users: the chat template hoists every system message to the top, breaking prompt caching and driving up costs and latency for both self-hosted and hosted deployments. The fix: use the latest_reminder role, which D...
A new MiniMax model is expected in the coming days. Behind the frenzy of Chinese launches is a deliberate push to move LLMs toward on-premise deployment, where data sovereignty and domestic hardware dictate the rules of the game.
