Bitnet B1.58 2B 4T GGUF — Hardware Requirements & GPU Compatibility
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577 downloads
9 likesSpecifications
- Publisher
- tdh111
- Parameters
- 2B
- Release Date
- 2025-05-16
- License
- MIT
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HuggingFace
How Much VRAM Does Bitnet B1.58 2B 4T GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 4.4 GB | — | 4.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Bitnet B1.58 2B 4T GGUF?
BF16 · 4.4 GBBitnet B1.58 2B 4T GGUF (BF16) requires 4.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Bitnet B1.58 2B 4T GGUF?
BF16 · 4.4 GB33 devices with unified memory can run Bitnet B1.58 2B 4T GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Bitnet B1.58 2B 4T GGUF need?
Bitnet B1.58 2B 4T GGUF requires 4.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2B × 16 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF164.4 GB- Can I run Bitnet B1.58 2B 4T GGUF on a Mac?
Bitnet B1.58 2B 4T GGUF requires at least 4.4 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 Bitnet B1.58 2B 4T GGUF locally?
Yes — Bitnet B1.58 2B 4T GGUF can run locally on consumer hardware. At BF16 quantization it needs 4.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Bitnet B1.58 2B 4T GGUF?
At BF16, Bitnet B1.58 2B 4T GGUF can reach ~663 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~149 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 ÷ 4.4 × 0.55 = ~663 tok/s
Estimated speed at BF16 (4.4 GB)
AMD Instinct MI300X~663 tok/sNVIDIA GeForce RTX 4090~149 tok/sNVIDIA H100 SXM~495 tok/sAMD Instinct MI250X~410 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Bitnet B1.58 2B 4T GGUF?
At BF16, the download is about 4.00 GB.
- Which GPUs can run Bitnet B1.58 2B 4T GGUF?
35 consumer GPUs can run Bitnet B1.58 2B 4T GGUF at BF16 (4.4 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 Bitnet B1.58 2B 4T GGUF?
33 devices with unified memory can run Bitnet B1.58 2B 4T GGUF at BF16 (4.4 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.