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🦙Reddit r/LocalLLaMA•Aug 29, 2026Freshcollected in 8h

Hy4-preview Shrunk 87% with Minimal Performance Loss

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🦙Read original on Reddit r/LocalLLaMA

#model-compression#quantization#local-inferencetencent-hy4-previewtencenthy4-previewgguf

💡A 1.5 TB model reportedly fits into a 200 GB GGUF while keeping 98% performance.

⚡ 30-Second TL;DR

What Changed

Model size was reduced from about 1.5 TB to approximately 200 GB.

Why It Matters

If the reported numbers hold across independent benchmarks, this could significantly lower storage, transfer, and deployment barriers for very large models. However, practitioners should verify quality across their own workloads before treating the compression as near-lossless.

What To Do Next

Download the compressed Hy4-preview GGUF and benchmark it against the original on your target prompts, measuring quality, RAM, and loading time.

Who should care:Developers & AI Engineers

Key Points

•Model size was reduced from about 1.5 TB to approximately 200 GB.
•The compressed GGUF reportedly preserves around 98% of performance.
•The smaller artifact could improve feasibility for local and hybrid deployments.

🧠 Deep Insight

Background and context from public sources — not the original article. 8 sources cited.

🔑 Enhanced Key Takeaways

•Hy4-preview utilizes a Mixture-of-Experts (MoE) architecture with 770 billion total parameters, activating only 49 billion parameters per token.
•The model features a 1-million-token context window specifically optimized for long-horizon software engineering and document analysis.
•Tencent implemented Gated DeepSeek Sparse Attention (DSA) and identity Hyper-Connections (iHC) across 78 layers to enhance information flow.
•The model includes a native 10-billion parameter Multi-Token Prediction (MTP) layer designed to facilitate efficient speculative decoding.
•Hy4-preview was released under the Apache 2.0 license, with weights hosted on Hugging Face, ModelScope, and GitCode.

📊 Competitor Analysis▸ Show

Feature	Hy4-preview	GLM-5.3	Kimi K3
Architecture	770B MoE	Proprietary	Proprietary
Benchmark Score	2.99	2.92	2.94
Context Window	1M tokens	N/A	N/A

🛠️ Technical Deep Dive

Architecture: 770B parameter MoE with 49B active parameters per token.
Layers: 78 layers utilizing Gated DeepSeek Sparse Attention (DSA) and identity Hyper-Connections (iHC).
Speculative Decoding: Native 10B parameter MTP layer with pre-configured recipes for vLLM and SGLang.
Modes: Dual-mode operation featuring high-effort reasoning for complex tasks and no_think mode for low-latency responses.
Quantization: Official FP8 variant provided to enable edge and local deployment.

🔮 Future ImplicationsAI analysis grounded in cited sources

Hy4-preview will become the standard for local enterprise document analysis.

The combination of a 1M token context window and the ability to run on 200GB of VRAM makes it uniquely suited for private, local RAG pipelines.

Tencent will see increased adoption in the open-source developer ecosystem.

The Apache 2.0 licensing and native support for standard inference engines like vLLM lower the barrier to entry for high-parameter model deployment.

⏳ Timeline

2026-08

Tencent officially releases Hy4-preview under the Apache 2.0 license.

📎 Sources (8)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

🦙Read original article on Reddit r/LocalLLaMA

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Original source: Reddit r/LocalLLaMA ↗

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