EDGE OSOPEN-WEIGHT LLM COMPARISON
gpt-oss-120b vs gpt-oss-20bSpecs, VRAM and the hardware to run them
OpenAI’s two open-weight reasoning models share the same design, 128K context and Apache 2.0 license. gpt-oss-120b has 117B parameters (5.1B active) and fits on one 80 GB GPU; gpt-oss-20b has 21B (3.6B active) and runs on a 16 GB-class GPU.
Parameters
117B vs 21B
Active per token
5.1B vs 3.6B
Context
128K vs 128K
Memory, 1 user
70 GB vs 15 GB
Which should you choose?
CHOOSE GPT-OSS-120B IF
You want stronger reasoning and have a single 80 GB-class GPU such as an H100, or a DGX Spark.
CHOOSE GPT-OSS-20B IF
You need a model for laptops, small GPUs or edge devices, or want to serve many users cheaply.
Still deciding between gpt-oss-120b and gpt-oss-20b?
Tell us your models, users and data. We will recommend a setup and run it inside your own building with Vanik Edge OS. Free, no obligation.
gpt-oss-120b vs gpt-oss-20b specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | gpt-oss-120b | gpt-oss-20b |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Total parameters | 117B | 21B |
| Active per token | 5.1B | 3.6B |
| Design | Mixture of experts | Mixture of experts |
| Experts | 128 experts, 4 active per token | 32 experts, 4 active per token |
| Context window | 128K | 128K |
| Input and output | Text | Text |
| Layers | 36 layers | 24 layers |
| Attention | Alternating full and 128-token sliding-window attention; 8 KV heads, head size 64 | Alternating full and 128-token sliding-window attention; 8 KV heads, head size 64 |
| License | Apache 2.0 | Apache 2.0 |
| Released weight formats | MXFP4 (native) | MXFP4 (native) |
| Weights at MXFP4 / MXFP4 | About 66 GB | About 13 GB |
VRAM needed to run them
GPU memory at the lowest released precision. Each user needs their own KV cache, so memory grows with users and context length.
| GPU memory needed (MXFP4 / MXFP4) | gpt-oss-120b | gpt-oss-20b |
|---|---|---|
| 1 user, 8K context | About 70 GB | About 15 GB |
| 1 user, 128K context | About 75 GB | About 18 GB |
| 16 users, 8K context each | About 75 GB | About 18 GB |
| 64 users, 8K context each | About 90 GB | About 28 GB |
Which GPUs can run gpt-oss-120b and gpt-oss-20b
Smallest setup on each system, with 8K context per user. Green fits on one GPU or system, amber needs several in one server, grey needs more than one 8-GPU server.
| Hardware | gpt-oss-120b: 1 user | gpt-oss-120b: 16 users | gpt-oss-20b: 1 user | gpt-oss-20b: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
| NVIDIA L40S 48 GB per GPU | 2 GPUs | 2 GPUs | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA H100 NVL 94 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA H200 NVL 141 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| AMD Instinct MI300X 192 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| AMD Instinct MI325X 256 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA DGX B200 1.44 TB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
Setups in amber or grey need several GPUs or servers working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
Which is faster?
On the same hardware, gpt-oss-20b should generate tokens about 1.3× faster for each user: it reads about 2.2 GB of weights per token (3.6B active at MXFP4) versus 2.9 GB for gpt-oss-120b. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies. Real throughput also depends on batching, the inference engine and the GPU.
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
gpt-oss-120b vs gpt-oss-20b questions
What is the difference between gpt-oss-120b and gpt-oss-20b?
OpenAI’s two open-weight reasoning models share the same design, 128K context and Apache 2.0 license. gpt-oss-120b has 117B parameters (5.1B active) and fits on one 80 GB GPU; gpt-oss-20b has 21B (3.6B active) and runs on a 16 GB-class GPU.
gpt-oss-120b vs gpt-oss-20b: which is better to run on-premise?
Choose gpt-oss-120b if you want stronger reasoning and have a single 80 GB-class GPU such as an H100, or a DGX Spark. Choose gpt-oss-20b if you need a model for laptops, small GPUs or edge devices, or want to serve many users cheaply.
Which needs less VRAM, gpt-oss-120b or gpt-oss-20b?
gpt-oss-20b. For one user with 8K context it needs about 15 GB, versus about 70 GB for gpt-oss-120b. For 16 users it needs about 18 GB versus 75 GB.
Which is faster, gpt-oss-120b or gpt-oss-20b?
On the same hardware, gpt-oss-20b should generate tokens about 1.3× faster for each user: it reads about 2.2 GB of weights per token (3.6B active at MXFP4) versus 2.9 GB for gpt-oss-120b. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
Can I run gpt-oss-120b locally?
Yes. At MXFP4 it needs about 70 GB of GPU memory for one user with 8K context, which fits on 1 NVIDIA H100 NVL. A team of 16 needs about 75 GB: 1 NVIDIA H100 NVL.
Can I run gpt-oss-20b locally?
Yes. At MXFP4 it needs about 15 GB of GPU memory for one user with 8K context, which fits on 1 NVIDIA L40S. A team of 16 needs about 18 GB: 1 NVIDIA L40S.
Can I use gpt-oss-120b and gpt-oss-20b commercially?
gpt-oss-120b is released under Apache 2.0. gpt-oss-20b is released under Apache 2.0. Check each license before a commercial deployment.
How are these numbers calculated?
Memory figures come from our VRAM calculator method: weights at the lowest released precision, plus the KV cache for each user’s context, plus about 5 to 10% runtime overhead, keeping 10% of each GPU free. Where a developer does not publish the cache size we estimate it from the architecture. These are planning estimates, not benchmarks.
Related comparisons
Sources: Hugging Face: openai/gpt-oss-120b; OpenAI: gpt-oss model card. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.