text model · Hunyuan · Windows

Can I run Hunyuan-A13B-Instruct on AMD Ryzen AI Halo (128GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated

Yes. Hunyuan-A13B-Instruct runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~48.3 GB of ~96 GB usable).

Needs ~48.3 GB Device usable ~96 GB

Runs at Q4_K_M using ~48.3 GB of ~96 GB usable. You have room for Q8_0 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. AMD Ryzen AI Halo (128GB) leaves ~47.7 GB of headroom, room to step up to Q8_0 for higher quality.

Q4_K_M needed: ~48.3 GB
Usable on device: ~96 GB
Device memory: 128 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~96 GB usable

Q2_K

~36.4 GB

Q3_K_M

~42 GB

Q4_K_M

~48.3 GB

Q5_K_M

~59.9 GB

Q6_K

~68.5 GB

Q8_0

~82.5 GB

FP16

~163.9 GB

The line marks AMD Ryzen AI Halo (128GB)'s ~96 GB budget; rungs past it are too large.

Run it

Install commands Windows

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf tencent/Hunyuan-A13B-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get tencent/Hunyuan-A13B-Instruct-GGUF

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model Hunyuan

Parameters: 80B (MoE, 13B active)
Q4_K_M size: 45.43 GB
Q8_0 size: 79.58 GB
Context: 256k

Full Hunyuan-A13B-Instruct requirements →

Device Windows

Memory: 128 GB unified
Usable for weights: ~96 GB
Power draw: ~120 W
Best runtime: llama.cpp (Vulkan/ROCm) / LM Studio

Best models for AMD Ryzen AI Halo (128GB) →

You could also run

Run Hunyuan-A13B-Instruct on other hardware

Apple M4 Max (128GB)Apple M5 Max (128GB)Apple M3 Ultra (256GB)

FAQ

Can AMD Ryzen AI Halo (128GB) run Hunyuan-A13B-Instruct?

Yes. Hunyuan-A13B-Instruct runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~48.3 GB of ~96 GB usable).

How much memory does Hunyuan-A13B-Instruct need?

AMD Ryzen AI Halo (128GB) has room to spare. At Q4_K_M the weights are ~45.43 GB; with KV cache and runtime overhead, budget ~48.3 GB at a 4k context. It is a Mixture-of-Experts model (80B total / 13B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Hunyuan-A13B-Instruct on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Markdown

[![Hunyuan-A13B-Instruct on AMD Ryzen AI Halo (128GB)](https://localmodel.run/badge/hunyuan-a13b/amd-ryzen-ai-halo-128gb.svg)](https://localmodel.run/can-i-run/hunyuan-a13b/amd-ryzen-ai-halo-128gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.