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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 4K)Best on 24 GB (BF16 · ~303 tok/s · 4K)Macs from 8 GB (BF16 · ~33 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Bitnet B1.58 2B 4T
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve microsoft/bitnet-b1.58-2B-4T --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (9 GB headroom) · BF16
- Generation speed
- ~108 tok/s
- generation speed
- Cost per 1M output tokens
- $0.15
- per 1M output tokens
How Much VRAM Does Bitnet B1.58 2B 4T Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~303 tok/s |
Selected BF16
Brain floating point 162.3GBat 4K context
- Weights
- 1.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.7 GB free · ~303 tok/s
≥50 tok/s needs ≥166 GB/s · RTX 5090 ~539, RTX 3090 Ti ~303
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Bitnet B1.58 2B 4T, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.7 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~229Simulate output on RTX 3080RTX 2080 Ti~185Simulate output on RTX 2080 TiRTX 3070 Ti~183Simulate output on RTX 3070 TiM3*8 GB~33Simulate output on iMac 24" M3 (8 GB)Orin Nano~31Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~22Simulate output on iPhone 1720 GB
fits · 17.7 GB free · up to 4K ctx
1 GPU
RX 7900 XT~222Simulate output on RX 7900 XT32–40 GB
fits · 29.7 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~539Simulate output on RTX 5090M5 Max36 GB~149Simulate output on Mac Studio M5 Max (36 GB)M532 GB~50Simulate output on MacBook Air 13" M5 (32 GB)Orin~62Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~41Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 0.8BAlibaba | — | 0.9 GB | ~753 | same size |
Specifications
- Publisher
- Microsoft
- Parameters
- 850M
- Architecture
- BitNetForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-04-15
- License
- MIT
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
Can I run Bitnet B1.58 2B 4T on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Bitnet B1.58 2B 4T. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 2.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Bitnet B1.58 2B 4T locally?
Yes — Bitnet B1.58 2B 4T can run locally on consumer hardware. 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 ~2222 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 ÷ 2.16 × 0.65 = ~2407 tok/s
Estimated speed at BF16 (2.2 GB)
Real-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?
52 consumer GPUs can run Bitnet B1.58 2B 4T at BF16. 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 Bitnet B1.58 2B 4T?
163 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, 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.