EDGE OSOPEN-WEIGHT LLM COMPARISON
Gemma 4 31B vs gpt-oss-120bSpecs, VRAM and the hardware to run them
Gemma 4 31B is Google’s 30.7B dense model with image input and a 256K context; gpt-oss-120b is OpenAI’s 117B mixture-of-experts reasoning model with 5.1B active and 128K context. Both are Apache 2.0 and fit on a single 80 GB GPU.
Parameters
30.7B vs 117B
Active per token
30.7B vs 5.1B
Context
256K vs 128K
Memory, 1 user
20 GB vs 70 GB
Which should you choose?
CHOOSE GEMMA 4 31B IF
You need image understanding, a longer context, or a model that fits on a single 32 GB GPU at 4-bit.
CHOOSE GPT-OSS-120B IF
You want a text reasoning model that generates tokens fast because only 5.1B parameters are active per token.
Still deciding between Gemma 4 31B and gpt-oss-120b?
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Gemma 4 31B vs gpt-oss-120b specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | Gemma 4 31B | gpt-oss-120b |
|---|---|---|
| Developer | OpenAI | |
| Total parameters | 30.7B | 117B |
| Active per token | 30.7B (dense) | 5.1B |
| Design | Dense | Mixture of experts |
| Experts | None (dense) | 128 experts, 4 active per token |
| Context window | 256K tokens | 128K |
| Input and output | Text and image input, text output | Text |
| Layers | 60 layers | 36 layers |
| Attention | Local sliding-window attention (1,024 tokens) interleaved with global attention; the final layer is always global | Alternating full and 128-token sliding-window attention; 8 KV heads, head size 64 |
| License | Apache 2.0 | Apache 2.0 |
| Released weight formats | BF16, FP8, 4-bit | MXFP4 (native) |
| Weights at 4-bit / MXFP4 | About 17 GB | About 66 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 (4-bit / MXFP4) | Gemma 4 31B | gpt-oss-120b |
|---|---|---|
| 1 user, 8K context | About 20 GB | About 70 GB |
| 1 user, 128K context | About 25 GB | About 75 GB |
| 16 users, 8K context each | About 32 GB | About 75 GB |
| 64 users, 8K context each | About 68 GB | About 90 GB |
Which GPUs can run Gemma 4 31B and gpt-oss-120b
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 | Gemma 4 31B: 1 user | Gemma 4 31B: 16 users | gpt-oss-120b: 1 user | gpt-oss-120b: 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 | Yes, 1 GPU | Yes, 1 GPU | 2 GPUs | 2 GPUs |
| 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-120b should generate tokens about 5.9× faster for each user: it reads about 2.9 GB of weights per token (5.1B active at MXFP4) versus 17 GB for Gemma 4 31B. 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.
Gemma 4 31B vs gpt-oss-120b questions
What is the difference between Gemma 4 31B and gpt-oss-120b?
Gemma 4 31B is Google’s 30.7B dense model with image input and a 256K context; gpt-oss-120b is OpenAI’s 117B mixture-of-experts reasoning model with 5.1B active and 128K context. Both are Apache 2.0 and fit on a single 80 GB GPU.
Gemma 4 31B vs gpt-oss-120b: which is better to run on-premise?
Choose Gemma 4 31B if you need image understanding, a longer context, or a model that fits on a single 32 GB GPU at 4-bit. Choose gpt-oss-120b if you want a text reasoning model that generates tokens fast because only 5.1B parameters are active per token.
Which needs less VRAM, Gemma 4 31B or gpt-oss-120b?
Gemma 4 31B. For one user with 8K context it needs about 20 GB, versus about 70 GB for gpt-oss-120b. For 16 users it needs about 32 GB versus 75 GB.
Which is faster, Gemma 4 31B or gpt-oss-120b?
On the same hardware, gpt-oss-120b should generate tokens about 5.9× faster for each user: it reads about 2.9 GB of weights per token (5.1B active at MXFP4) versus 17 GB for Gemma 4 31B. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
Can I run Gemma 4 31B locally?
Yes. At 4-bit it needs about 20 GB of GPU memory for one user with 8K context, which fits on 1 NVIDIA L40S. A team of 16 needs about 32 GB: 1 NVIDIA L40S.
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 use Gemma 4 31B and gpt-oss-120b commercially?
Gemma 4 31B is released under Apache 2.0. gpt-oss-120b 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: Google: Gemma 4 model card; Hugging Face: google/gemma-4-31B; Hugging Face: openai/gpt-oss-120b. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.