Bitnet B1.58 2B 4T — Hardware Requirements & GPU Compatibility
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Bitnet B1.58 2B 4T is a 850M-parameter open language model from Microsoft. It supports a context window of up to 4,096 tokens. At BF16 it needs about 2.16 GB of VRAM — see which GPUs and Macs can run it below.
8.8K downloads
1.5K likesSpecifications
- Publisher
- Microsoft
- Parameters
- 850M
- Architecture
- BitNetForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-04-15
- License
- MIT
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HuggingFace
How Much VRAM Does Bitnet B1.58 2B 4T Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 2.2 GB | 2.3 GB | 1.70 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 Bitnet B1.58 2B 4T?
BF16 · 2.2 GBBitnet B1.58 2B 4T (BF16) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 2.3 GB. 50 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?
BF16 · 2.2 GB59 devices with unified memory can run Bitnet B1.58 2B 4T, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Bitnet B1.58 2B 4T
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Bitnet B1.58 2B 4T need?
Bitnet B1.58 2B 4T requires 2.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 850M × 16 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
BF162.2 GBBF16 + full context2.3 GB- Can I run Bitnet B1.58 2B 4T on a Mac?
Bitnet B1.58 2B 4T requires at least 2.2 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 Bitnet B1.58 2B 4T locally?
Yes — Bitnet B1.58 2B 4T can run locally on consumer hardware. At BF16 quantization it needs 2.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Bitnet B1.58 2B 4T?
At BF16, Bitnet B1.58 2B 4T can reach ~2037 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~303 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 ÷ 2.2 × 0.65 = ~2407 tok/s
Estimated speed at BF16 (2.2 GB)
~2407 tok/s~303 tok/s~2407 tok/s~2037 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?
At BF16, the download is about 1.70 GB.
- Which GPUs can run Bitnet B1.58 2B 4T?
50 consumer GPUs can run Bitnet B1.58 2B 4T at BF16 (2.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Bitnet B1.58 2B 4T?
59 devices with unified memory can run Bitnet B1.58 2B 4T at BF16 (2.2 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.