LLM GPU requirements
gpt-oss-120b GPU requirements
How much GPU memory gpt-oss-120b needs in BF16, FP8 and 4-bit, how its KV cache grows with context, what fine-tuning takes, and which servers run it, with monthly prices.
- Parameters
- 117B 5.1B active
- Architecture
- MoE 36 layers
- KV heads
- 8 × 64 dim
- Context
- 128k tokens
Cheapest servers that run it
MXFP4 (native 4-bit) weights · 8k context · 8 concurrent requests · about 68 GB
-
NVIDIA RTX PRO 6000 Blackwell In stock
96 GB GDDR7 · Single GPU
$1,390/mo
-
NVIDIA H100 SXM In stock
80 GB HBM3 · Single GPU
$2,190/mo
-
NVIDIA H200 In stock
141 GB HBM3e · Single GPU
$3,090/mo
Delivery date confirmed before you pay. Missed it? Full refund.
Inference memory
How much VRAM gpt-oss-120b needs
Weights take parameters × bytes per parameter. Serving adds the KV cache and about 10% runtime overhead; the totals below assume an 8k context with 8 concurrent requests and a BF16 KV cache.
| Precision | Weights | Total to serve | Cheapest server that fits |
|---|---|---|---|
| BF16 2 bytes per parameter | 234 GB | 261 GB | 8× NVIDIA RTX PRO 6000 Blackwell $10,490/mo |
| FP8 1 byte per parameter | 117 GB | 132 GB | NVIDIA B200 $4,590/mo |
| MXFP4 / 4-bit 0.5 bytes per parameter | 59 GB | 68 GB | NVIDIA RTX PRO 6000 Blackwell $1,390/mo |
Estimates with about 8% of GPU memory kept free. Quantized weights also keep some layers in higher precision, and frameworks differ in overhead; check the free memory your engine reports at startup. A model split across GPUs with tensor parallelism runs best on NVLink servers (HGX); the 8× RTX PRO 6000 server links its GPUs over PCIe.
KV cache
gpt-oss-120b KV cache by context length
Each token in flight stores keys and values for 18 full-attention layers: 2 × 18 layers × 8 KV heads × 64 dimensions × 2 bytes = 36 KB per token in BF16. An FP8 KV cache halves these figures.
| Context per request | 1 request | 8 requests | 32 requests |
|---|---|---|---|
| 4k tokens | 0.2 GB | 1.2 GB | 4.8 GB |
| 32k tokens | 1.2 GB | 9.7 GB | 39 GB |
| 128k tokens | 4.8 GB | 39 GB | 155 GB |
The sliding-window layers keep only the last 128 tokens, which adds a few megabytes per request and is left out above.
Fine-tuning
Memory to fine-tune gpt-oss-120b
Rough totals before tuning batch size and sequence length: QLoRA keeps 4-bit base weights, LoRA keeps BF16 weights frozen, and a full fine-tune with AdamW needs about 16 bytes per parameter plus activations, sharded across GPUs.
| Method | Approximate memory | Cheapest server that fits |
|---|---|---|
| QLoRA (4-bit base) | 88 GB | NVIDIA H200 $3,090/mo |
| LoRA (BF16 base) | 290 GB | 8× NVIDIA RTX PRO 6000 Blackwell $10,490/mo |
| Full fine-tune (AdamW, mixed precision) | 2,246 GB | Multi-node: GPU clusters |
Full fine-tuning assumes sharded training (FSDP or ZeRO-3) with activation checkpointing. See the fine-tuning guide for methods and settings.
About gpt-oss-120b
gpt-oss-120b is OpenAI’s open-weight Mixture-of-Experts model: about 117B parameters with roughly 5.1B active per token, 36 layers that alternate full attention and a 128-token sliding window, and Apache 2.0 weights. OpenAI releases the expert weights in MXFP4, a 4-bit format, and states that the model runs on a single 80 GB GPU.
Every figure on this page comes from the model’s published architecture and the same formulas as our VRAM calculator, where you can change the context length, concurrency and KV cache precision.
Model facts
- Developer
- OpenAI
- License
- Apache 2.0
- Type
- Mixture of Experts
- Parameters
- 117Babout 5.1B active per token
- Layers
- 36alternating full and sliding-window attention
- Attention
- 64 query heads, 8 KV headsgrouped-query attention, head dimension 64
- Context window
- 128k tokens
FAQ
gpt-oss-120b GPU FAQ
The short version. Anything else, our engineers reply 24/7.
Ask an engineerHow much VRAM does gpt-oss-120b need?
About 234 GB for the weights in BF16, 117 GB in FP8 and 59 GB in 4-bit, plus the KV cache: about 0.0 GB per 1,000 tokens in flight with a BF16 cache. Serving it in MXFP4 (native 4-bit) with an 8k context and 8 concurrent requests takes about 68 GB in total.
Can gpt-oss-120b run on a single GPU?
Yes. In MXFP4 (native 4-bit), a single NVIDIA RTX PRO 6000 Blackwell (96 GB) serves it with an 8k context and 8 concurrent requests. Longer contexts or more users need more memory for the KV cache, so check your case in the VRAM calculator.
What is the cheapest FullGPU server for gpt-oss-120b?
For MXFP4 (native 4-bit) serving with an 8k context and 8 concurrent requests, the NVIDIA RTX PRO 6000 Blackwell at $1,390 per month is the lowest-priced configuration that fits. For other contexts and concurrency, size it in the VRAM calculator or ask an engineer.
How much memory do I need to fine-tune gpt-oss-120b?
Roughly 88 GB with QLoRA, 290 GB with LoRA on BF16 weights, and 2,246 GB for a full fine-tune with AdamW in mixed precision, before tuning batch size and sequence length. Full fine-tunes of large models are sharded across GPUs with FSDP or ZeRO-3.
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