EDGE OSGOOGLE · 30.7B PARAMETERS
Gemma 4 31B hardware and VRAM requirements
Google’s largest Gemma 4 model: a 30.7B dense model with image understanding and a 256K-token context. Gemma 4 31B is a dense model with all 30.7B parameters work on every token. At FP8 it needs about 36 GB of GPU memory for one user, and about 47 GB to serve a team of 16 with 8K context each.
Total parameters
30.7B
Active per token
30.7B (dense)
Context length
256K tokens
Released as
BF16
Minimum VRAM
20 GB at 4-bit
For a team of 16
47 GB at FP8
VRAM needed to run Gemma 4 31B
GPU memory by precision, number of concurrent users and context length. Each user needs their own KV cache, so memory grows with users and context.
| Precision | Weights only | 1 user, 8K | 16 users, 8K | 16 users, 32K | LoRA fine-tune |
|---|---|---|---|---|---|
| BF16 Released | 61 GB | 69 GB | 81 GB | 97 GB | 73 GB |
| FP8 Common quantization | 31 GB | 36 GB | 47 GB | 63 GB | 41 GB |
| 4-bit Community quantization (estimated) | 17 GB | 20 GB | 32 GB | 48 GB | 26 GB |
Full fine-tuning in mixed precision needs about 498 GB: weights, gradients and optimizer states at roughly 16 bytes per parameter. LoRA trains small adapter layers on a frozen copy of the model instead, so it needs far less.
GPUs that can run Gemma 4 31B on-premise
For a team of 16 using Gemma 4 31B at FP8 with 8K context each (about 47 GB), smallest fit first.
NVIDIA H100 NVL
94 GB per GPU · 38 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · 40 GB to spare.
NVIDIA DGX Spark
128 GB per system · 62 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · 80 GB to spare.
AMD Instinct MI300X
192 GB per GPU · 126 GB to spare.
AMD Instinct MI325X
256 GB per GPU · 184 GB to spare.
NVIDIA DGX B200
1.44 TB per system · 1.25 TB to spare.
NVIDIA L40S
48 GB per GPU · Split across 2 GPUs in one server, 40 GB to spare.
Some of these setups need several GPUs 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
Weighing two of these? Compare AI hardware side by side, for example H100 vs H200, RTX PRO 6000 vs RTX 5090 or DGX Spark vs Mac Studio.
Running Gemma 4 31B for your team?
We will size the GPU server for your users and data, and set up Gemma 4 31B on it inside your own building. Free, no obligation.
Gemma 4 31B at a glance
Compare Gemma 4 31B with other models
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 GPU questions
How do I run Gemma 4 31B locally?
To run Gemma 4 31B locally you need about 20 GB of GPU memory at 4-bit for one user (47 GB for a team of 16), system RAM of at least 17 GB so the weights can be loaded, around 17 GB of fast NVMe storage for the model files, and an inference engine such as vLLM or SGLang. A team of 16 fits on NVIDIA H100 NVL.
Is Gemma 4 free?
Yes. Gemma 4 is released under the Apache 2.0 license, so the weights are free to download, self-host and use commercially.
Can Gemma 4 generate images?
No. Gemma 4 31B understands text and images but only produces text.
Is Gemma 4 open source?
Yes. Google released Gemma 4’s weights under the Apache 2.0 license.
How big is Gemma 4 31B?
Gemma 4 31B has 30.7B parameters in total (dense: all of them work on every token). The weights take about 17 GB at 4-bit.
How much RAM do I need for Gemma 4 31B?
System RAM should be at least as large as the model files so they can be loaded: about 17 GB at 4-bit, or 31 GB at FP8. For fast responses, though, the model has to fit in GPU memory (VRAM), not just RAM; running from RAM and CPU is many times slower.
How much VRAM to run Gemma 4 31B?
Gemma 4 31B needs about 36 GB of GPU memory at FP8 for one user with 8K context: 31 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 47 GB.
Can Gemma 4 31B run on a single GPU?
Yes. At FP8 with one user and 8K context it needs about 36 GB, which fits on a single NVIDIA L40S or NVIDIA H100 NVL.
Can I fine-tune Gemma 4 31B?
LoRA fine-tuning at FP8 needs roughly 41 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 498 GB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 40.0 KB per token per user. We add about 10% of the weights plus 1 GB for runtime overhead and keep 10% of each GPU free. Figures are planning estimates; real usage depends on the inference engine and settings.
VRAM for similar models
Specifications from Google: Gemma 4 model card and Hugging Face: google/gemma-4-31B. Memory and bandwidth are manufacturer figures; GPU counts are rounded to 1, 2, 4 or 8 per server. All figures are planning estimates. Last checked November 10, 2026.
Run Gemma 4 31B inside your own building
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, so your data never leaves.