Opt 125M — Hardware Requirements & GPU Compatibility
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Meta OPT 125M is a 125-million parameter language model from Meta's Open Pre-trained Transformer (OPT) project. Released in 2022, it was part of Meta's effort to provide the research community with openly available large language models that replicate the performance of GPT-3 class models at various scales. As one of the smallest models in the OPT family, the 125M variant is primarily useful for research, experimentation, and educational purposes. It can run on virtually any hardware, including CPU-only setups. While significantly less capable than modern models, it remains a useful reference point in LLM research.
6.8M downloads
234 likesSpecifications
- Publisher
- Meta
- Parameters
- 125M
- Architecture
- OPTForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,272
- Release Date
- 2023-09-15
- License
- Other
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HuggingFace
How Much VRAM Does Opt 125M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 0.3 GB | — | 0.25 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Opt 125M?
FP16 · 0.3 GBOpt 125M (FP16) requires 0.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 Opt 125M?
FP16 · 0.3 GB33 devices with unified memory can run Opt 125M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Opt 125M need?
Opt 125M requires 0.3 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 125M × 16 bits ÷ 8 = 0.3 GB
VRAM usage by quantization
FP160.3 GB- Can I run Opt 125M on a Mac?
Opt 125M requires at least 0.3 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 125M locally?
Yes — Opt 125M can run locally on consumer hardware. At FP16 quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Opt 125M?
At FP16, Opt 125M can reach ~10411 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~2340 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 ÷ 0.3 × 0.55 = ~10411 tok/s
Estimated speed at FP16 (0.3 GB)
AMD Instinct MI300X~10411 tok/sNVIDIA GeForce RTX 4090~2340 tok/sNVIDIA H100 SXM~7781 tok/sAMD Instinct MI250X~6437 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Opt 125M?
At FP16, the download is about 0.25 GB.