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.
10.5M downloads
266 likesSpecifications
- Publisher
- Meta
- Parameters
- 125M
- Architecture
- OPTForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,272
- Release Date
- 2022-05-11
- 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 |
|---|---|---|---|---|---|
| FP16est. | 16.00 | 0.3 GB | — | 0.25 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 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. 50 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 GB59 devices with unified memory can run Opt 125M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently 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 ~15714 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.3 × 0.65 = ~18571 tok/s
Estimated speed at FP16 (0.3 GB)
~18571 tok/s~2340 tok/s~18571 tok/s~15714 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.
- Which GPUs can run Opt 125M?
50 consumer GPUs can run Opt 125M at FP16 (0.3 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 Opt 125M?
59 devices with unified memory can run Opt 125M at FP16 (0.3 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.