gpt-oss-120b The memory it needs, what you may do with it, and the hardware it suits.
Short answer. gpt-oss-120b needs about 68 GB of memory at 4-bit, 126 GB at 8-bit and 235 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 116.8 billion parameters and a 131,072-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, ANVE Office only with partial offload to system memory, which is much slower, ANVE Office Pro (GPU) only with partial offload to system memory, which is much slower. These are estimates from its published size, not benchmarks.
01
What it is
gpt-oss-120b is a language model for chat, drafting and question answering, published by OpenAI on Hugging Face on 4 August 2025. It has about 116.8 billion parameters and a configured context window of 131,072 tokens. It is a mixture-of-experts model: 128 experts, 4 used for each token.
Its card lists the licence as Apache 2.0. In short, the licence allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant.
Hugging Face counted about 4,188,657 downloads of it in the 30 days before we checked (7 Oct 2026), and 5,368 people have liked it there.
02
How much memory it needs
The arithmetic: parameters × bits per weight ÷ 8 gives the weights; add the KV cache, which grows with every token of context; then add about 1.5 GB for the runtime. These are estimates for planning, not measurements.
Each token of context adds about 0.04 MB of KV cache at 16-bit, so 32,768 tokens would add about 1.2 GB. 18 of its 36 layers use sliding-window attention, so the real cache is smaller than this full-attention estimate.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
234 GB
0.30 GB
235 GB
8-bit (Q8_0)
124 GB
0.30 GB
126 GB
4-bit (Q4_K)
66 GB
0.30 GB
68 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
GptOssForCausalLM
Model type
gpt_oss
Layers
36
Hidden size
2,880
Attention heads
64
Key-value heads
8 (grouped-query attention)
Head size
64
Vocabulary
201,088
Experts
128, 4 active per token
Configured context
131,072
04
Will it fit on ANVE?
These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 128 GB of system memory. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.
Configuration (at launch)
Memory for the model
Verdict
ANVE One
about 48 GB of 64 GB unified memory
does not fit
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
only with partial offload to system memory, which is much slower
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
only with partial offload to system memory, which is much slower
05
What an office would use it for
Models this large are stronger on long, hard drafting and reasoning, but need most of a 64 to 128 GB machine even at 4-bit, which leaves less room for long documents and several users.
06
Similar models to compare
GLM-4.5-Air (110.5B parameters, about 64 GB at 4-bit before context; commercial use allowed)
Nemotron-3-Super-120B-A12B (123.6B parameters, about 71 GB at 4-bit before context; custom licence: check before commercial use)
sarvam-105b (106.0B parameters, about 61 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does gpt-oss-120b need?
About 68 GB at 4-bit, 126 GB at 8-bit and 235 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can gpt-oss-120b run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 68 GB, so part of it would sit in system memory, which is much slower.
Is gpt-oss-120b free for commercial use?
Its card lists Apache 2.0, which allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant. Read the licence itself before relying on this summary; it is not legal advice.
What is gpt-oss-120b’s context window?
Its configuration sets 131,072 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
08
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
