Opt 1.3B — Hardware Requirements & GPU Compatibility
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Opt 1.3B is a 1.3B-parameter open language model from Meta. It supports a context window of up to 2,048 tokens. At FP16 it needs about 2.86 GB of VRAM — see which GPUs and Macs can run it below.
92.5K downloads
186 likesSpecifications
- Publisher
- Meta
- Parameters
- 1.3B
- Architecture
- OPTForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,272
- Release Date
- 2022-05-11
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Opt 1.3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16est. | 16.00 | 2.9 GB | — | 2.60 GB | Full half-precision — baseline for inference |
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 Opt 1.3B?
FP16 · 2.9 GBOpt 1.3B (FP16) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ 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 Opt 1.3B?
FP16 · 2.9 GB59 devices with unified memory can run Opt 1.3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Opt 1.3B need?
Opt 1.3B requires 2.9 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 16 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP162.9 GB- Can I run Opt 1.3B on a Mac?
Opt 1.3B requires at least 2.9 GB at FP16, 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 Opt 1.3B locally?
Yes — Opt 1.3B can run locally on consumer hardware. At FP16 quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Opt 1.3B?
At FP16, Opt 1.3B can reach ~1678 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~229 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 ÷ 2.9 × 0.65 = ~1818 tok/s
Estimated speed at FP16 (2.9 GB)
~1818 tok/s~229 tok/s~1818 tok/s~1678 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Opt 1.3B?
At FP16, the download is about 2.60 GB.
- Which GPUs can run Opt 1.3B?
52 consumer GPUs can run Opt 1.3B at FP16 (2.9 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 Opt 1.3B?
59 devices with unified memory can run Opt 1.3B at FP16 (2.9 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.