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Can the LattePanda Sigma (32 GB) run Hunyuan Hy4 Preview (780B)?

67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Hunyuan Hy4 Preview (780B) needs about 315 GB even at Q2_K; the LattePanda Sigma (32 GB) has 16.0 GB usable.

Download Hunyuan Hy4 Preview (780B)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/AngelSlim/Hy4-preview-GGUF (opens in a new tab)

ollama run hf.co/AngelSlim/Hy4-preview-GGUF

Original weights · Hugging Face

Full-precision files from Tencent, for fine-tuning, vLLM, Transformers.

huggingface.co/tencent/Hy4-preview (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Tencent. The weights themselves download from Hugging Face.

github.com/Tencent-Hunyuan/Hy4-preview (opens in a new tab)

git clone https://github.com/Tencent-Hunyuan/Hy4-preview

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run
How well it runs on the LattePanda Sigma (32 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.1 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score9.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the LattePanda Sigma (32 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 315 GB 16.0 GB — Won't run 67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 385 GB 16.0 GB — Won't run 80Great quality (A) · 80 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 478 GB 16.0 GB — Won't run 91Excellent quality (A+) · 91 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 556 GB 16.0 GB — Won't run 93Excellent quality (A+) · 93 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 642 GB 16.0 GB — Won't run 95Excellent quality (A+) · 95 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 829 GB 16.0 GB — Won't run 96Excellent quality (A+) · 96 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 1,563 GB 16.0 GB — Won't run 96Excellent quality (A+) · 96 of 100: how capable the model is at F16, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

Models that do run on the LattePanda Sigma (32 GB)

Quick answers

How much memory does Hunyuan Hy4 Preview (780B) need on the LattePanda Sigma 32GB?

Even at Q2_K it needs about 315 GB; the LattePanda Sigma 32GB has 16.0 GB usable.

What can the LattePanda Sigma 32GB run instead?

Gemma 4 26B-A4B, Maple Preview (20B-A1B) and KAT-Coder V2.5 Dev (35B-A3B) run best on it; the page lists more, each with its speed.

What hardware can run Hunyuan Hy4 Preview (780B)?

M3 Ultra 512GB, M5 Ultra 512GB and AMD EPYC 7002 / 7003 series NAS 512GB run it; the page lists more, each with its speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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