Hunyuan 0.5B Instruct — Hardware Requirements & GPU Compatibility
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Hunyuan 0.5B Instruct is a 539M-parameter open language model from tencent. It supports a context window of up to 262,144 tokens. At BF16 it needs about 1.48 GB of VRAM — see which GPUs and Macs can run it below.
471 downloads
57 likesSpecifications
- Publisher
- tencent
- Parameters
- 539M
- Architecture
- HunYuanDenseV1ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 120,818
- Release Date
- 2025-08-06
Get Started
HuggingFace
How Much VRAM Does Hunyuan 0.5B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 1.5 GB | 14.3 GB | 1.08 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Hunyuan 0.5B Instruct?
BF16 · 1.5 GBHunyuan 0.5B Instruct (BF16) requires 1.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 14.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Hunyuan 0.5B Instruct?
BF16 · 1.5 GB33 devices with unified memory can run Hunyuan 0.5B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Hunyuan 0.5B Instruct need?
Hunyuan 0.5B Instruct requires 1.5 GB of VRAM at BF16. Full 262K context adds up to 12.8 GB (14.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 539M × 16 bits ÷ 8 = 1.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
BF161.5 GBBF16 + full context14.3 GB- Can I run Hunyuan 0.5B Instruct on a Mac?
Hunyuan 0.5B Instruct requires at least 1.5 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Hunyuan 0.5B Instruct locally?
Yes — Hunyuan 0.5B Instruct can run locally on consumer hardware. At BF16 quantization it needs 1.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hunyuan 0.5B Instruct?
At BF16, Hunyuan 0.5B Instruct can reach ~1970 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~443 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 1.5 × 0.55 = ~1970 tok/s
Estimated speed at BF16 (1.5 GB)
~1970 tok/s~443 tok/s~1472 tok/s~1218 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Hunyuan 0.5B Instruct?
At BF16, the download is about 1.08 GB.
- Which GPUs can run Hunyuan 0.5B Instruct?
35 consumer GPUs can run Hunyuan 0.5B Instruct at BF16 (1.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Hunyuan 0.5B Instruct?
33 devices with unified memory can run Hunyuan 0.5B Instruct at BF16 (1.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.