Tools / LLM GPU calculator / gpt-oss-20b

gpt-oss-20b VRAM requirements

GPU memory for the weights and KV cache at FP16, FP8 and 4-bit, and how many GPUs that takes. Figures from the model's config.json.

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Short answer

gpt-oss-20b needs about 47 GB of GPU memory at FP16/BF16, 23 GB at FP8 and 13 GB at 4-bit to serve one user at an 8K-token context. Its 20.9B parameters take 41.8 GB at 16-bit, and that conversation adds 0.2 GB of KV cache. It is a mixture-of-experts model: 3.6B parameters are active per token, but all 20.9B must be in GPU memory. On NVIDIA H100 80 GB GPUs that is 1 GPU at FP16 or FP8; for a desktop, at 4-bit it fits a single 24 GB GeForce RTX 4090.

Updated October 16, 2026. KV cache at FP16, plus 10% headroom for activations and the runtime.

GPU memory by precision

Weights	Weight memory	1 user, 8K context	32 users, 8K context	1 user, 128K context
FP16/BF16	41.8 GB	47 GB	54 GB	50 GB
FP8	20.9 GB	23 GB	30 GB	27 GB
4-bit (INT4)	11.5 GB	13 GB	20 GB	16 GB

Required memory = (weights + KV cache) ÷ 0.9. An FP8 KV cache halves the cache share; set it in the calculator.

How many GPUs for one user at 8K context

GPU	Memory	FP16/BF16	FP8	4-bit (INT4)
Data center · tensor parallel
NVIDIA L40S	48 GB	1 GPU	1 GPU	1 GPU
NVIDIA RTX PRO 6000 Blackwell Server Edition	96 GB	1 GPU	1 GPU	1 GPU
NVIDIA H100 SXM	80 GB	1 GPU	1 GPU	1 GPU
NVIDIA H100 NVL	94 GB	1 GPU	1 GPU	1 GPU
NVIDIA H200	141 GB	1 GPU	1 GPU	1 GPU
NVIDIA B200	180 GB	1 GPU	1 GPU	1 GPU
NVIDIA B300	288 GB	1 GPU	1 GPU	1 GPU
AMD Instinct MI300X	192 GB	1 GPU	1 GPU	1 GPU
AMD Instinct MI355X	288 GB	1 GPU	1 GPU	1 GPU
Workstation & consumer · layer split
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	96 GB	1 card	1 card	1 card
NVIDIA GeForce RTX 3090	24 GB	2 cards	1 card	1 card
NVIDIA GeForce RTX 4090	24 GB	2 cards	1 card	1 card
NVIDIA GeForce RTX 5090	32 GB	2 cards	1 card	1 card
Apple Mac Studio M3 Ultra / M5 Ultra	96 GB (72 usable)	1 Mac	1 Mac	1 Mac
Apple Mac Studio M3 Ultra / M5 Ultra	256 GB (192 usable)	1 Mac	1 Mac	1 Mac
Apple Mac Studio M5 Ultra	512 GB (384 usable)	1 Mac	1 Mac	1 Mac

Data center GPUs are counted at a tensor-parallel size (TP) that divides the model’s attention heads, within one 8-GPU server. Workstation and consumer cards are counted for a layer split, which works with any number of cards. Mac Studio counts about 75% of unified memory as usable by the GPU. Capacity only: speed depends on memory bandwidth and the serving stack.

Memory is step one. Throughput, latency, power and the network decide the real build.

Size my actual workload

gpt-oss-20b architecture

Parameters: 20.9B total, 3.6B active per token (mixture of experts)
Layers with a KV cache: 24
Attention heads / KV heads: 64 / 8
Head dimension: 64
Attention: 12 sliding-window layers (128 tokens) + 12 global
Maximum context: 131,072 tokens
KV cache per token (FP16): 2 × 12 global layers × 8 KV heads × 64 head dim × 2 bytes = 24,576 bytes (0.02 MB) per token, plus 2,048 bytes per token in each of the 12 sliding-window layers, for at most 128 tokens

Source: openai/gpt-oss-20b config.json on Hugging Face. Parameter counts are the Hub’s count of the repo’s safetensors files, or the model card’s figure.

Questions about gpt-oss-20b

How much VRAM does gpt-oss-20b need?

About 47 GB at FP16/BF16, 23 GB at FP8 and 13 GB at 4-bit, for one user at an 8K-token context. That is 41.8 GB, 20.9 GB or 11.5 GB of weights, plus 0.2 GB of KV cache, plus 10% headroom for activations and the runtime. Serving 32 users at 8K each takes 54 GB at FP16 or 30 GB at FP8, because every conversation keeps its own KV cache.

How many H100 GPUs does gpt-oss-20b need?

At FP16/BF16 it takes 1 GPU, for one user at 8K or for 32 users. At FP8, 1 GPU, for one user at 8K or for 32 users. One user at FP8 takes 1 GPU on H200 (141 GB) and 1 GPU on B200 (180 GB).

Can gpt-oss-20b run on an RTX 4090 or RTX 5090?

At 4-bit it needs about 13 GB for one user at 8K: a single RTX 4090 (24 GB) or a single RTX 5090 (32 GB). At FP8 (23 GB) it takes a single RTX 4090 (24 GB) or a single RTX 5090 (32 GB).

Can gpt-oss-20b run on a Mac Studio?

macOS lets the GPU use about 75% of unified memory by default: about 72 GB of a 96 GB, 192 GB of a 256 GB and 384 GB of a 512 GB Mac Studio. At 4-bit gpt-oss-20b needs 13 GB for one user at 8K, so it fits one 96 GB Mac Studio; at FP8 (23 GB) it fits one 96 GB Mac Studio.

How large is gpt-oss-20b's KV cache?

12 of its 24 layers use a sliding window and cache at most 128 tokens; only the other 12 keep the whole context. At FP16 that is 2 × 12 global layers × 8 KV heads × 64 head dim × 2 bytes = 24,576 bytes (0.02 MB) per token, plus 2,048 bytes per token in each of the 12 sliding-window layers, for at most 128 tokens. An 8K-token conversation holds 0.2 GB at FP16, and a 128K-token one holds 3.2 GB.

Does gpt-oss-20b need memory for all its experts?

Yes. gpt-oss-20b is a mixture-of-experts model with 20.9B parameters, of which about 3.6B are active for each token. Any token can be routed to any expert, so all of them must be in GPU memory: memory follows total parameters, speed follows active ones.

What is gpt-oss-20b's maximum context length?

131,072 tokens, according to its Hugging Face configuration. One user at that full length holds 3.2 GB of KV cache at FP16, for 27 GB in total with FP8 weights, or 25 GB with an FP8 KV cache as well.

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