AllocateOpenAI GPT-OSS 120B GPU requirements
What it takes to run OpenAI GPT-OSS 120B locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
OpenAI GPT-OSS 120B serves up to 131,072 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: A100 (80 GB)
How it works
Common questions
How much VRAM does OpenAI GPT-OSS 120B need?
At 8K context: roughly 268 GB at FP16, 134 GB at 8-bit, and 74 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run OpenAI GPT-OSS 120B?
At 4-bit, yes: a A100 (80 GB) or larger handles it at 8K context. At FP16 you need a Mac M3 Ultra (512 GB unified) or larger.
Does quantization hurt OpenAI GPT-OSS 120B's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is OpenAI GPT-OSS 120B under?
Custom license. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for OpenAI GPT-OSS 120B?
Yes: managed inference. Allocate serves OpenAI GPT-OSS 120B token-metered from $0.15 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like OpenAI GPT-OSS 120B token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.