Self-host an open model
Self-host GPT-OSS 120B: GPU, VRAM, and rental cost
Direct answer
Self-hosting GPT-OSS 120B (120.4B, 5.1B active) in FP8 needs about 161 GB of VRAM per GPU at 8K context and 8 concurrent requests. It fits on 1 tracked GPU on a single card, the cheapest being the B200 (HGX, per GPU) from about $5.89 per GPU-hour. Larger context or higher concurrency grows the KV cache and can push it past one card into tensor parallelism. This is a fit and rental-rate estimate, not a throughput quote; use the calculators below for cost per token.
Estimate cost per 1M tokens - Self-host GPT-OSS 120B serving cost →
GPUs that hold GPT-OSS 120B on one card
Single-GPU fit in FP8 at 8K context, 8 concurrent requests, with the cheapest tracked rental rate.
| GPU | VRAM | Cheapest /GPU-hr | Provider |
|---|---|---|---|
| B200 (HGX, per GPU) | 180 GB | $5.89 | RunPod |
Frequently asked questions
What GPU do I need to run GPT-OSS 120B?
In FP8 at 8K context, GPT-OSS 120B needs about 161 GB of VRAM per GPU. The cheapest single GPU that holds it is the B200 (HGX, per GPU) (180 GB) from around $5.89 per GPU-hour. Higher context or concurrency needs more VRAM or tensor parallelism.
How is the VRAM figure calculated?
Model weights (parameters times bytes per weight for the precision) plus the KV cache (from the model's real layers, KV heads, head dimension, and attention pattern) plus activation and a safety margin. Architecture comes from the model's Hugging Face config; GPU VRAM from the NVIDIA datasheet.
Cite this page
Self-host GPT-OSS 120B: GPU and VRAM. ByteCosts. Updated June 18, 2026. https://bytecosts.com/gpu/self-host/gpt-oss-120b/