Macbert4csc Base Chinese — Hardware Requirements & GPU Compatibility
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Macbert4csc Base Chinese is a 102M-parameter open language model from shibing624. It supports a context window of up to 512 tokens. At BF16 it needs about 0.23 GB of VRAM — see which GPUs and Macs can run it below.
542.9K downloads
121 likesSpecifications
- Publisher
- shibing624
- Parameters
- 102M
- Architecture
- BertForMaskedLM
- Context Length
- 512 tokens
- Vocabulary Size
- 21,128
- Release Date
- 2022-03-02
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Macbert4csc Base Chinese Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.2 GB | — | 0.20 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 Macbert4csc Base Chinese?
BF16 · 0.2 GBMacbert4csc Base Chinese (BF16) requires 0.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Macbert4csc Base Chinese?
BF16 · 0.2 GB59 devices with unified memory can run Macbert4csc Base Chinese, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Macbert4csc Base Chinese need?
Macbert4csc Base Chinese requires 0.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 102M × 16 bits ÷ 8 = 0.2 GB
VRAM usage by quantization
BF160.2 GB- Can I run Macbert4csc Base Chinese on a Mac?
Macbert4csc Base Chinese requires at least 0.2 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 Macbert4csc Base Chinese locally?
Yes — Macbert4csc Base Chinese can run locally on consumer hardware. At BF16 quantization it needs 0.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Macbert4csc Base Chinese?
At BF16, Macbert4csc Base Chinese can reach ~20870 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2849 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 ÷ 0.2 × 0.65 = ~22609 tok/s
Estimated speed at BF16 (0.2 GB)
~22609 tok/s~2849 tok/s~22609 tok/s~20870 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Macbert4csc Base Chinese?
At BF16, the download is about 0.20 GB.
- Which GPUs can run Macbert4csc Base Chinese?
52 consumer GPUs can run Macbert4csc Base Chinese at BF16 (0.2 GB). 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 Macbert4csc Base Chinese?
59 devices with unified memory can run Macbert4csc Base Chinese at BF16 (0.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.