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 in the Hunyuan family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 0.72 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~910 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~100 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Hunyuan 0.5B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve tencent/Hunyuan-0.5B-Instruct --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~325 tok/s
- generation speed
- Cost per 1M output tokens
- $0.071
- per 1M output tokens
How Much VRAM Does Hunyuan 0.5B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 2.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~1040 tok/s | |
| 0.7 GB | 2.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~993 tok/s | |
| 0.7 GB | 2.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~910 tok/s | |
| 0.8 GB | 2.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~840 tok/s | |
| 0.8 GB | 2.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~771 tok/s | |
| 0.9 GB | 2.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~697 tok/s | |
| 1.5 GB | 3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~443 tok/s |
Selected Q4_K_M
4-bit medium quantization1.4GBat 16K context
- Weights
- 0.3 GB
- KV cache
- 0.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.6 GB free · ~910 tok/s
≥50 tok/s needs ≥55 GB/s · RTX 5090 ~1618, RTX 3090 Ti ~910
Which Hardware Can Run Hunyuan 0.5B Instruct?
Q4_K_M · 0.7 GBHunyuan 0.5B Instruct (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 13.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Hunyuan 0.5B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.6 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~686Simulate output on RTX 3080RTX 2080 Ti~556Simulate output on RTX 2080 TiRTX 3070 Ti~549Simulate output on RTX 3070 TiM3*8 GB~100Simulate output on iMac 24" M3 (8 GB)Orin Nano~92Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~66Simulate output on iPhone 1720 GB
fits · 18.6 GB free · up to 256K ctx
1 GPU
RX 7900 XT~667Simulate output on RX 7900 XT32–40 GB
fits · 30.6 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1618Simulate output on RTX 5090M5 Max36 GB~447Simulate output on Mac Studio M5 Max (36 GB)M532 GB~149Simulate output on MacBook Air 13" M5 (32 GB)Orin~185Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~122Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Hunyuan 0.5B Instruct
Hunyuan family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SmolVLM2 500M Video InstructHugging Face | — | 0.9 GB | ~950 | same size |
| SmolVLM 500M InstructHugging Face | — | 0.9 GB | ~950 | same size |
| Falcon H1 0.5B BaseTII UAE | — | 1.2 GB | ~950 | same size |
| TIPO 500M FtKBlueLeaf | — | 0.8 GB | ~809 | same size |
| H2o Danube3 500M Chath2oai | — | 1 GB | ~923 | same size |
| Pythia 410MEleutherAI | — | 0.3 GB | ~1986 | same size |
Specifications
- Publisher
- Tencent
- Family
- Hunyuan
- Parameters
- 539M
- Architecture
- HunYuanDenseV1ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 120,818
- Release Date
- 2025-07-30
Frequently Asked Questions
How much VRAM does Hunyuan 0.5B Instruct need?
Hunyuan 0.5B Instruct requires 0.7 GB of VRAM at Q4_K_M, or 1.5 GB at BF16. Full 262K context adds up to 12.8 GB (13.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 539M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Hunyuan 0.5B Instruct?
For Hunyuan 0.5B Instruct, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Hunyuan 0.5B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Hunyuan 0.5B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Hunyuan 0.5B Instruct locally?
Yes — Hunyuan 0.5B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Hunyuan 0.5B Instruct?
At Q4_K_M, Hunyuan 0.5B Instruct can reach ~6667 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~910 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 0.72 × 0.65 = ~7222 tok/s
Estimated speed at Q4_K_M (0.7 GB)
Real-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 Q4_K_M, the download is about 0.32 GB. The full-precision BF16 version is 1.08 GB. The smallest option (Q2_K) is 0.23 GB.
Which GPUs can run Hunyuan 0.5B Instruct?
52 consumer GPUs can run Hunyuan 0.5B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Hunyuan 0.5B Instruct?
163 devices with unified memory can run Hunyuan 0.5B Instruct at Q4_K_M (0.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.