Phala Releases Quantized GLM-5.3-W4AFP8 Optimized for Agents
bgmshana · x · 2026-09-01
Phala Network released a quantized version of GLM-5.3, named GLM-5.3-W4AFP8, built directly from the BF16 master.
Technical Specs:
- MoE expert weights use group-128 INT4 with AWQ.
- Activations and non-expert layers use FP8.
Calibration:
- Uses coding-agent traces from SWE-chat.
- Aligned specifically for long-horizon agent workloads.
More from Infra
- TensorSharp vs llama.cpp: Qwen 3.8 Flash Next Benchmarks — fuzhongkai · 2026-09-01
- 2 engineers + AI designed a working LLM chip in 2 weeks, no human in the loop — 新智元 · 2026-09-01
- Why did increasing context size increase speed in Llama.cpp? — satnl · 2026-09-01
- AI inference demand surges again, supply brutally outpaced by token growth — Baconbrix · 2026-09-01
- Warp founder predicts cloud-based collaborative factories for all companies within a year — charlieholtz · 2026-09-01
- JPM: 1GW of AI Infrastructure Costs $40-45B, Frontier Labs Make ~$30B per GW — zephyr_z9 · 2026-09-01