Opt 350M — Hardware Requirements & GPU Compatibility
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Meta OPT 350M is a 350-million parameter language model from Meta's Open Pre-trained Transformer (OPT) project, released in 2022 as part of a suite of models ranging from 125M to 175B parameters. It was designed to provide researchers with open access to models comparable to GPT-3 at various scales. The 350M variant runs on minimal hardware and is suitable for research, prototyping, and educational use. While it has been surpassed by modern architectures in terms of capability, it remains a lightweight option for basic text generation experiments and as a benchmark baseline.
156.3K downloads
149 likesSpecifications
- Publisher
- Meta
- Parameters
- 350M
- 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 350M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 0.8 GB | — | 0.70 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Opt 350M?
FP16 · 0.8 GBOpt 350M (FP16) requires 0.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 350M?
FP16 · 0.8 GB33 devices with unified memory can run Opt 350M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Opt 350M need?
Opt 350M requires 0.8 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 350M × 16 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP160.8 GB- Can I run Opt 350M on a Mac?
Opt 350M requires at least 0.8 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 350M locally?
Yes — Opt 350M can run locally on consumer hardware. At FP16 quantization it needs 0.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Opt 350M?
At FP16, Opt 350M can reach ~3786 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~851 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 ÷ 0.8 × 0.55 = ~3786 tok/s
Estimated speed at FP16 (0.8 GB)
~3786 tok/s~851 tok/s~2830 tok/s~2341 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Opt 350M?
At FP16, the download is about 0.70 GB.
- Which GPUs can run Opt 350M?
35 consumer GPUs can run Opt 350M at FP16 (0.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Opt 350M?
33 devices with unified memory can run Opt 350M at FP16 (0.8 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.