Can you run Hy3 locally?

Yes, on high-memory hardware. Hy3 (Tencent Hunyuan 3) is a ~295B Mixture-of-Experts model, but at a 1-bit quantization it needs only about 87 GB, which fits a 128 GB unified-memory machine such as an AMD Strix Halo, a 128 GB Mac, or an NVIDIA RTX Spark. A single normal GPU cannot hold it.

What hardware can run Hy3?

A 128 GB unified-memory machine (AMD Strix Halo / Ryzen AI Max+, a 128 GB Apple Mac, or NVIDIA RTX Spark) can run Hy3 at 1-bit or 2-bit quantization. Higher-quality quantizations (Q3, Q4) need a workstation or multi-GPU server. A single consumer GPU is not enough.

Hy3 is released under the Apache 2.0 license with open weights, and is available as GGUF quantizations on Hugging Face, so you can download and run it locally for free if you have enough memory.

Can You Run Hy3 (Tencent's 295B Model) Locally?

Q: How much memory does Hy3 need?

Computed from its real GGUF files, Hy3 needs about 87 GB at 1-bit (IQ1_M), about 95 GB at 2-bit, about 103 GB at Q2_K, about 134 GB at Q3_K_M, and about 170 GB at Q4_K_M, including KV cache and a system margin at a 4,096-token context.

By Lefi Abdelmonem

Author · AI Local Check · Published July 18, 2026

Hy3 — Tencent's Hunyuan 3 — is one of the most downloaded open-weight models on the hub right now, with a single GGUF repo already past 800,000 downloads. It's a giant: roughly 295 billion parameters. So can you actually run it locally? Unlike most models this size, the answer is a surprising yes — on the right hardware.

What is Hy3?

Hy3 is a Mixture-of-Experts (MoE) model with about 295 billion total parameters, of which only around 21 billion are active per token — so it computes much faster than a dense 295B model would. It's released under the Apache 2.0 license (fully open weights), supports a 262,144-token context, and is available as GGUF for llama.cpp.

Key facts
• Maker: Tencent (Hunyuan 3)
• Size: ~295B parameters (MoE, ~21B active)
• License: Apache 2.0 (open weights)
• Context: 262,144 tokens · Format: GGUF

How much memory does Hy3 need?

Computed from its real GGUF files, the low-bit quantizations are what make Hy3 special:

Quantization	Memory to load	Fits on
IQ1_M (~1-bit)	~87 GB	128 GB unified-memory machine
IQ2_M (~2-bit)	~95 GB	128 GB unified-memory machine
Q2_K	~103 GB	128 GB (tight)
Q3_K_M	~134 GB	Workstation / multi-GPU
Q4_K_M	~170 GB	Server-scale

The hardware that changes everything: 128 GB unified memory

Here's the twist. At a 1-bit quantization, Hy3 needs only about 87 GB — which fits in a 128 GB unified-memory machine. In 2026, those are real and affordable-ish: the AMD Strix Halo (Ryzen AI Max+, up to 128 GB), Apple's 128 GB Macs, and NVIDIA's RTX Spark all pool a large unified memory the model can live in.

That makes Hy3 arguably the first near-300B model within reach at home. Contrast it with the other giants of 2026 — Kimi K3 (2.8T, ~226 GB even at 1-bit), DeepSeek V4 and Inkling — which stay out of reach. Hy3's smaller size plus 1-bit quantization is what tips it over the line.

Honest caveats: a normal GPU won't do — even an RTX 5090 (32 GB) can't hold Hy3 alone. You need a 128 GB unified-memory system, 1-bit quantization loses some quality, and speed will be modest. But it runs, privately, on your own box.

How to check what fits your hardware

See the exact per-quantization requirements on the Hy3 model page, learn what the labels mean in GGUF quantization explained, or check any model against your own setup.

The bottom line

Hy3 is a ~295B open-weight model that, thanks to 1-bit quantization (~87 GB) and the new wave of 128 GB unified-memory machines, you can genuinely run at home — a first for a model this size. Don't have 128 GB? Plenty of smaller models fit a normal GPU. Check what your PC can run.

→ Check what your exact GPU can run