XiaoHong V1 — Hardware Requirements & GPU Compatibility
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177 downloads
2 likesSpecifications
- Publisher
- CongJ-Pan
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-03-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does XiaoHong V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 17.0 GB | 22.7 GB | 16.38 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run XiaoHong V1?
BF16 · 17.0 GBXiaoHong V1 (BF16) requires 17.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 41K context window can add up to 5.7 GB, bringing total usage to 22.7 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run XiaoHong V1?
BF16 · 17.0 GB21 devices with unified memory can run XiaoHong V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does XiaoHong V1 need?
XiaoHong V1 requires 17.0 GB of VRAM at BF16. Full 41K context adds up to 5.7 GB (22.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 16 bits ÷ 8 = 16.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.3 GB (at full 41K context)
VRAM usage by quantization
BF1617.0 GBBF16 + full context22.7 GB- Can I run XiaoHong V1 on a Mac?
XiaoHong V1 requires at least 17.0 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 XiaoHong V1 locally?
Yes — XiaoHong V1 can run locally on consumer hardware. At BF16 quantization it needs 17.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is XiaoHong V1?
At BF16, XiaoHong V1 can reach ~172 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 17.0 × 0.55 = ~172 tok/s
Estimated speed at BF16 (17.0 GB)
AMD Instinct MI300X~172 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~128 tok/sAMD Instinct MI250X~106 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of XiaoHong V1?
At BF16, the download is about 16.38 GB.
- Which GPUs can run XiaoHong V1?
6 consumer GPUs can run XiaoHong V1 at BF16 (17.0 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run XiaoHong V1?
21 devices with unified memory can run XiaoHong V1 at BF16 (17.0 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.