Hunyuan Hy3 models — VRAM fit & GPU picks

Tencent's Hy3 is a 295B MoE at 256K context under Apache-2.0. One of the largest permissively licensed open weights; realistic serving is multi-GPU or cloud.

Hunyuan is Tencent's open-weight LLM family, and its current flagship, Hy3, sits at the very top of what permissively licensed open models offer: a 295B-parameter mixture-of-experts model that activates only 21B parameters per token, with a 256K context window and an Apache-2.0 license. That combination — frontier-class total capacity, sparse activation, long context, and a license that allows commercial use — makes Hunyuan Hy3 a community favorite for anyone building on open weights at the largest scale.

It is also, bluntly, not a model you run on a gaming PC. The Q4 quantized weights alone take about 162 GB, so this family is aimed at multi-GPU workstations, high-memory Macs, and cloud instances rather than consumer desktops. Below we break down the VRAM math and the realistic ways to run it.

Size ladder

The family currently has one model, at one extreme of the size spectrum:

Hy3 (295B total, MoE, 21B active per token, 192 experts / 8 per token) — Q4 weights ≈ 162 GB; Q4 @ 8K context needs ≈ 167.5 GB VRAM → requires the 192GB+ tier. FP16 weights would be ≈ 590 GB, which is multi-node territory. Hy3 detail page

What makes Hunyuan Hy3 stand out

MoE efficiency. With 21B active parameters out of 295B total, Hy3 delivers quality closer to a giant dense model while paying compute costs closer to a ~21B one. That sparse activation is the whole reason a model this large is servable at all outside a datacenter.

256K context. The native context window is 256K tokens — long enough for entire codebases, book-length documents, or extended agentic sessions. Note the trade-off: KV cache grows with context, so at the full 256K the cache alone adds roughly 134 GB on top of the Q4 weights. Long-context runs push memory well past 300 GB.

Apache-2.0 license. No usage restrictions for commercial deployment, fine-tuning, or redistribution. Among open weights in this size class, a fully permissive license is a genuine differentiator.

GGUF ecosystem. As with other popular open families, community GGUF quants let you run Hy3 in llama.cpp-style setups, offloading layers across GPUs or partially to system RAM when VRAM falls short.

Which one should you pick

There is only one size to pick — the real question is whether your hardware can hold it.

12GB / 16GB / 24GB GPUs: Not viable for Hy3 at any practical quantization. Look at smaller families (7B–30B class) instead.
48GB / 96GB: Still short of the ~167.5 GB needed for Q4 at 8K context. Aggressive sub-Q4 quants shrink weights further but with real quality loss at this scale.
192GB+ (multi-GPU workstations, Mac Studio 192GB/512GB class, cloud A100/H100 nodes): This is where Hy3 becomes realistic. A pair of 96GB GPUs or a 192GB unified-memory Mac holds Q4 weights plus KV cache at moderate context; cloud instances are the practical route for full 256K workloads.

If you specifically want MoE models with lighter hardware requirements, check the smaller MoE families on the site — Hunyuan is the heavyweight end of that category.

Links

Models in this family

1 models in the Hunyuan Hy3 family, grouped by series. Q4 (GB) is weights-only; total VRAM adds KV cache and overhead — the fit check links compute it for a representative retail GPU at 8K context.