Open 1B Base — Hardware Requirements & GPU Compatibility
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Open 1B Base is a 1.6B-parameter open language model from Gensyn. It supports a context window of up to 4,096 tokens. At BF16 it needs about 3.62 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
7 likesSpecifications
- Publisher
- Gensyn
- Parameters
- 1.6B
- Architecture
- Open1BForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-09-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Open 1B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 3.6 GB | 3.7 GB | 3.22 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 Open 1B Base?
BF16 · 3.6 GBOpen 1B Base (BF16) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 3.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Open 1B Base?
BF16 · 3.6 GB59 devices with unified memory can run Open 1B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Open 1B Base need?
Open 1B Base requires 3.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.6B × 16 bits ÷ 8 = 3.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
BF163.6 GBBF16 + full context3.7 GB- Can I run Open 1B Base on a Mac?
Open 1B Base requires at least 3.6 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 Open 1B Base locally?
Yes — Open 1B Base can run locally on consumer hardware. At BF16 quantization it needs 3.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Open 1B Base?
At BF16, Open 1B Base can reach ~1326 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~181 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 ÷ 3.6 × 0.65 = ~1437 tok/s
Estimated speed at BF16 (3.6 GB)
~1437 tok/s~181 tok/s~1437 tok/s~1326 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Open 1B Base?
At BF16, the download is about 3.22 GB.
- Which GPUs can run Open 1B Base?
52 consumer GPUs can run Open 1B Base at BF16 (3.6 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 Open 1B Base?
59 devices with unified memory can run Open 1B Base at BF16 (3.6 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.