gpt-oss-120b VRAM needed and GPU rental cost
gpt-oss-120b has 116.8 billion parameters (from the model card). Weights alone take 233.6 GB at FP16. With a 20% allowance for the KV cache and activations, plan for about 280 GB of VRAM at FP16, 140 GB at FP8 and 70 GB at INT4. The cheapest public on-demand way to hold it at FP8 is 2x A100 at Thunder Compute, $2.18/hr.
- Parameters
- 116.8B
- From the model card
- FP16
- 280 GB
- Estimated VRAM
- FP8
- 140 GB
- Estimated VRAM
- INT4
- 70 GB
- Estimated VRAM
Memory and cost
Estimated VRAM and the cheapest public rental that fits
Each row is a way to run or tune gpt-oss-120b. The option is the cheapest on-demand price we track, from rows we rate high or medium confidence, on one node of up to 8 GPUs. Change the precision or hours in the calculator.
| Mode | Estimated VRAM | Cheapest public on-demand option | Per month (730 h) | Source |
|---|---|---|---|---|
| Inference, FP16 or BF16 | 280 GB | 4x A100 at Thunder Compute, $4.36 per hour | $3,182.80 | Source |
| Inference, FP8 or INT8 | 140 GB | 2x A100 at Thunder Compute, $2.18 per hour | $1,591.40 | Source |
| Inference, INT4 (4-bit) | 70 GB | 1x A100 at Thunder Compute, $1.09 per hour | $795.70 | Source |
| Fine-tune, LoRA (16-bit base) | 280 GB | 4x A100 at Thunder Compute, $4.36 per hour | $3,182.80 | Source |
| Fine-tune, QLoRA (4-bit base) | 70 GB | 1x A100 at Thunder Compute, $1.09 per hour | $795.70 | Source |
| Fine-tune, full (Adam, mixed precision) | 2243 GB | 8x B300 at Hyperstack, $59.20 per hour | $43,216.00 | Source |
Fit by chip
How many GPUs gpt-oss-120b needs, by chip
Number of GPUs whose combined memory covers the estimate. Compare the chips on their price pages.
| Precision | L40S 48 GB | A100 80 GB | H100 80 GB | H200 141 GB | B200 180 GB | MI300X 192 GB | B300 288 GB |
|---|---|---|---|---|---|---|---|
| FP16 (280 GB) | 6 | 4 | 4 | 2 | 2 | 2 | 1 |
| FP8 (140 GB) | 3 | 2 | 2 | 1 | 1 | 1 | 1 |
| INT4 (70 GB) | 2 | 1 | 1 | 1 | 1 | 1 | 1 |
FAQ
gpt-oss-120b questions
How much VRAM does gpt-oss-120b need?
About 280 GB at FP16, 140 GB at FP8 and 70 GB at INT4. These are estimates: parameters times bytes per parameter, plus 20% for the KV cache and activations. Long contexts and large batches need more.
Can gpt-oss-120b run on one H100?
An H100 has 80 GB. At FP16 it needs 4 H100 GPUs, at FP8 2 and at INT4 1, by this estimate.
What does it cost to rent a GPU for gpt-oss-120b?
At FP8 the cheapest public on-demand option we track is 2x A100 at Thunder Compute, $2.18/hr, about $1,591.40 for a 730-hour month. At INT4 it is 1x A100 at Thunder Compute, $1.09/hr. Prices change often, so check the source link on each row. Last checked Nov 9, 2026.
How are these estimates made?
Memory is the parameter count times bytes per parameter (2 for FP16, 1 for FP8, 0.5 for INT4), times 1.2. Fine-tuning uses 2 bytes for LoRA, 0.5 for QLoRA and 16 for full fine-tuning with Adam (2 weights, 2 gradients, 12 optimizer bytes), times 1.2. Real usage depends on the framework, context length and batch size.
Need this capacity?
Tell us the GPU, count, term, region and budget. We reply with the public prices that match and the checks to run before you pay. Free, and we do not sell compute.