Xing4.0 29B A4B Official Document Writing 8k — Hardware Requirements & GPU Compatibility
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Xing4.0 29B A4B Official Document Writing 8k is a 31.2B-parameter open language model from Kuromi22. It supports a context window of up to 262,144 tokens. At BF16 it needs about 63.90 GB of VRAM — see which GPUs and Macs can run it below.
126 downloads
4 likesSpecifications
- Publisher
- Kuromi22
- Parameters
- 31.2B
- Architecture
- Xing4_0ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-09-18
- License
- Apache 2.0
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How Much VRAM Does Xing4.0 29B A4B Official Document Writing 8k Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 63.9 GB | 213.1 GB | 62.43 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Xing4.0 29B A4B Official Document Writing 8k?
BF16 · 63.9 GBXing4.0 29B A4B Official Document Writing 8k (BF16) requires 63.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 84+ GB is recommended. Using the full 262K context window can add up to 149.2 GB, bringing total usage to 213.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Xing4.0 29B A4B Official Document Writing 8k?
BF16 · 63.9 GB22 devices with unified memory can run Xing4.0 29B A4B Official Document Writing 8k, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Xing4.0 29B A4B Official Document Writing 8k need?
Xing4.0 29B A4B Official Document Writing 8k requires 63.9 GB of VRAM at BF16. Full 262K context adds up to 149.2 GB (213.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31.2B × 16 bits ÷ 8 = 62.4 GB
KV Cache + Overhead ≈ 1.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 150.7 GB (at full 262K context)
VRAM usage by quantization
BF1663.9 GBBF16 + full context213.1 GB- Can NVIDIA GeForce RTX 5090 run Xing4.0 29B A4B Official Document Writing 8k?
No — Xing4.0 29B A4B Official Document Writing 8k requires at least 63.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Xing4.0 29B A4B Official Document Writing 8k on a Mac?
Xing4.0 29B A4B Official Document Writing 8k requires at least 63.9 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 Xing4.0 29B A4B Official Document Writing 8k locally?
Yes — Xing4.0 29B A4B Official Document Writing 8k can run locally on consumer hardware. At BF16 quantization it needs 63.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Xing4.0 29B A4B Official Document Writing 8k?
At BF16, Xing4.0 29B A4B Official Document Writing 8k can reach ~97 tok/s on AMD Instinct MI350X. 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 ÷ 63.9 × 0.65 = ~230 tok/s
Estimated speed at BF16 (63.9 GB)
~230 tok/s~230 tok/s~175 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Xing4.0 29B A4B Official Document Writing 8k?
At BF16, the download is about 62.43 GB.
- Which GPUs can run Xing4.0 29B A4B Official Document Writing 8k?
No single consumer GPU has enough VRAM to run Xing4.0 29B A4B Official Document Writing 8k at BF16 (63.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Xing4.0 29B A4B Official Document Writing 8k?
23 devices with unified memory can run Xing4.0 29B A4B Official Document Writing 8k at BF16 (63.9 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.