Opt 6.7B — Hardware Requirements & GPU Compatibility
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Opt 6.7B is a 6.7B-parameter open language model from Meta. It supports a context window of up to 2,048 tokens. At FP16 it needs about 14.74 GB of VRAM — see which GPUs and Macs can run it below.
58.7K downloads
121 likesSpecifications
- Publisher
- Meta
- Parameters
- 6.7B
- 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 6.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16est. | 16.00 | 14.7 GB | — | 13.40 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 6.7B?
FP16 · 14.7 GBOpt 6.7B (FP16) requires 14.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Opt 6.7B?
FP16 · 14.7 GB47 devices with unified memory can run Opt 6.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Opt 6.7B need?
Opt 6.7B requires 14.7 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 16 bits ÷ 8 = 13.4 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP1614.7 GB- Can I run Opt 6.7B on a Mac?
Opt 6.7B requires at least 14.7 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 6.7B locally?
Yes — Opt 6.7B can run locally on consumer hardware. At FP16 quantization it needs 14.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Opt 6.7B?
At FP16, Opt 6.7B can reach ~326 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~45 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 ÷ 14.7 × 0.65 = ~353 tok/s
Estimated speed at FP16 (14.7 GB)
~353 tok/s~45 tok/s~353 tok/s~326 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Opt 6.7B?
At FP16, the download is about 13.40 GB.
- Which GPUs can run Opt 6.7B?
26 consumer GPUs can run Opt 6.7B at FP16 (14.7 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Opt 6.7B?
49 devices with unified memory can run Opt 6.7B at FP16 (14.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.