🐉 Hunyuan 3 Local Guide: Can You Actually Run 295B at Home?

Tencent open-sourced Hunyuan 3 (Hy3) on July 6, 2026 under Apache-2.0, and the headline number — 295 billion parameters, 21 billion active — set off the usual round of "run this at home" posts. Most of them are wrong about the hardware. We've spent enough time watching people buy the wrong GPU on the strength of an active-parameter count that this guide leads with the memory math instead of the benchmark table.

By the numbers

Spec	Hunyuan 3 (Hy3)
Open-sourced	July 6, 2026
License	Apache-2.0
Architecture	Transformer MoE
Total parameters	295B
Active per forward pass	21B
Context window	256K tokens
SWE-bench (reported)	74.4%
Full-precision VRAM	~180GB+
Official low-bit quants	1-bit & 4-bit GGUF
Serving	vLLM 0.20.0+, llama.cpp

The memory trap, stated plainly

A mixture-of-experts model routes each token through a small subset of its experts. Hy3 activates 21B parameters per forward pass out of 295B total. That is a compute saving, not a memory saving.

Every one of those 295B parameters has to sit in addressable memory, because the router can select any expert for any token. You cannot page in experts on demand at conversational latency. So the honest requirement is: enough memory to hold 295B weights at whatever precision you're running, plus KV cache for your context length — and at 256K context the KV cache is not a rounding error.

At bf16 that's ~180GB before cache. Tencent's own deployment note recommends eight H20-3e-class GPUs with expert parallelism. That is a datacenter configuration, and no amount of clever quantization changes the shape of the problem — it only moves the number.

If you take one thing from this guide: active parameters tell you how fast it runs, total parameters tell you whether it runs at all. We wrote up the underlying arithmetic in quantization explained, and it applies to every MoE release this year.

What actually runs, by rig

Your hardware	Realistic Hy3 path
8× H100/H20 80GB	Full bf16, vLLM tensor-parallel 8, expert parallel, 256K context
128GB unified memory (Mac Studio) or RTX 6000-class	Official 4-bit GGUF via llama.cpp, reduced context
128GB system RAM, no big GPU	1-bit GGUF, CPU/hybrid offload — works, but slow enough to be a demo not a tool
24GB consumer GPU (3090/4090/5090)	Skip it. Run GLM-5.2 or a Qwen 3.6 27B instead
Rented cloud GPU	Cheapest way to evaluate before committing hardware

That fourth row is the one nobody wants to publish, so we'll be blunt: if you have a single 24GB card, Hunyuan 3 is not your model. There is no quant that fits 295B into 24GB and still behaves like Hy3. You will get better real-world output from a well-quantized 27B-32B dense model that fits comfortably — see our GPU buyer's guide for what each VRAM tier actually unlocks.

The 4-bit GGUF path (the one most readers want)

Tencent shipping official 1-bit and 4-bit GGUF quants alongside the weights is the genuinely useful part of this release. Vendor-produced quants tend to be better calibrated than community conversions in the first weeks, because the people who trained the model chose which layers to protect.

The workable single-machine setup:

1. Pull the 4-bit GGUF from the Hugging Face repo rather than converting yourself — the conversion of a 295B MoE is not a laptop job.
2. Run under llama.cpp with multi-token prediction enabled; this is the configuration Tencent targeted.
3. Cut your context first. Do not start at 256K. Start at 16K, confirm it loads and generates, then climb. KV cache at full context will eat more memory than people budget for.
4. Expect prompt processing to be the bottleneck, not token generation. Long documents will feel much slower than the tokens/sec number suggests.

If you've only ever run models through Ollama, note that this is a llama.cpp-first release; our Ollama vs llama.cpp comparison covers when dropping to the lower-level runtime is worth the friction, and this is squarely one of those times.

For the 8-GPU crowd: the vLLM config

vLLM 0.20.0 and later support the Hunyuan MoE family. The serving flags that matter are tensor parallelism across all eight cards, expert parallelism explicitly enabled, bf16 dtype, and a max model length you should raise deliberately rather than pinning to 262144 on day one. Enabling expert parallelism is not optional at this size — without it you serialize the routing and give away most of the MoE advantage.

How we assessed this

We did not train or fine-tune Hy3, and we're not going to pretend we ran a 295B model on a home rig. What we did do: cross-check Tencent's published deployment guidance against the vLLM release notes and the independent write-ups, then translate the requirements into the hardware tiers our readers actually own. The VRAM math is arithmetic, not opinion — it's reproducible from the parameter count and precision. Where a figure is Tencent's own claim rather than an independent measurement (the 74.4% SWE-bench result in particular), we've labelled it as reported.

What didn't make the cut

• Head-to-head benchmark tables against GPT-5-class models. Vendor-reported evals a few weeks post-release are marketing until third parties replicate them.
• Fine-tuning guidance. Nobody is LoRA-ing a 295B MoE on consumer hardware, and pretending otherwise wastes your time.
• The 1-bit quant as a serious recommendation. It loads. Output quality at 1-bit on a model this size degrades in ways that are hard to predict and harder to debug. Treat it as a curiosity.

Pros and cons

Worth your time if: you want a genuinely Apache-2.0 frontier-class model, you have workstation-tier memory or a cloud budget, or you're building something commercial where license terms are a real constraint.

Skip it if: your ceiling is a 24GB consumer card, you need long-context throughput today, or you want something that works out of the box in Ollama with one pull command.

The bigger picture is that Apache-2.0 at this scale is the actual news. Kimi K3 is larger and GLM-5.2 is easier to run, but neither hands you these terms. If you're picking a base to build a product on, license permissiveness compounds in a way that a few benchmark points don't.

Official weights and deployment notes live on Tencent's Hugging Face org; the vLLM docs cover the expert-parallel flags in detail.