Tencent Hunyuan's official 1-bit quantization shrinks Hy4-preview from 1.5TB to ~200GiB with near-lossless accuracy

Tencent Hunyuan released an official 1-bit quantization of Hy4-preview (770B MoE), compressing the model from 1.5TB to roughly 200GiB in GGUF format — about 7x compression — while benchmarks show near-lossless accuracy and roughly 98% overall performance retained. This official release substantially lowers the storage barrier for local deployment of very large models.

Confirmed

Hunyuan officially published the 1-bit quantized Hy4 (per @lakySK)
The compression uses MIX-STQ10, shrinking 1.5TB to 200GiB GGUF at 7x ratio (per @ChrisGPT, @RedditUsr2)
Rather than uniform low-bit quantization, the method treats bit-width allocation as an optimization problem: calibration data assigns per-layer precision, with insensitive layers aggressively compressed to 1.31-bit and sensitive layers kept around 2-bit (per @ChrisGPT)
Post-quantization benchmarks: MCP Atlas 83.7 → 83.2, SWE-Bench multi 82.9 → 81.3 (per @lakySK)
Minimal accuracy loss versus BF16 across benchmarks, 98% performance retained (per @lakySK, @RedditUsr2)

Why it matters

1.5TB-scale MoE models were previously impractical to store or run locally; 200GB makes local deployment feasible
The per-layer, calibration-driven non-uniform bit allocation offers a reusable recipe for high-ratio quantization of other giant models
An official quantized release means users get an accuracy-guaranteed lightweight version without relying on community quantizations

2026-08-29 ~ 2026-08-30 · 5 related posts

Primary sources

Tencent Hunyuan releases official 1-bit quantization for Hy4 with minimal accuracy loss — lakySK · 2026-08-29 20:05 (UTC+08:00)
Tencent compresses 1.5TB model to 200GB with minimal accuracy loss — ChrisGPT · 2026-08-29 14:06 (UTC+08:00)
Tencent compresses Hy4-preview from 1.5TB to 200GB GGUF — RedditUsr2 · 2026-08-29 22:31 (UTC+08:00)

[source] Tencent compresses 1.5TB model to 200GB with minimal accuracy loss — ChrisGPT · 2026-08-29
Tencent compresses 1.5TB model to 200GB with minimal benchmark loss via layer-wise calibration — ChrisGPT · 2026-08-29
[source] Tencent Hunyuan releases official 1-bit quantization for Hy4 with minimal accuracy loss — lakySK · 2026-08-29
[source] Tencent compresses Hy4-preview from 1.5TB to 200GB GGUF — RedditUsr2 · 2026-08-29
Hy4-preview quantized to 200GiB with minimal accuracy loss — TencentHunyuan · 2026-08-30