EDGE OSGOOGLE · 25.2B PARAMETERS
Gemma 4 26B A4B hardware and VRAM requirements
Google’s mixture-of-experts Gemma 4 model: 25.2B parameters with 3.8B active per token, aimed at consumer GPUs and workstations. Gemma 4 26B A4B is a mixture-of-experts model with 3.8B of its 25.2B parameters work on each token. At FP8 it needs about 28 GB of GPU memory for one user, and about 34 GB to serve a team of 16 with 8K context each.
Total parameters
25.2B
Active per token
3.8B
Context length
256K tokens
Released as
BF16
Minimum VRAM
16 GB at 4-bit
For a team of 16
34 GB at FP8
VRAM needed to run Gemma 4 26B A4B
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 | 50 GB | 54 GB | 60 GB | 68 GB | 58 GB |
| FP8 Common quantization | 25 GB | 28 GB | 34 GB | 42 GB | 32 GB |
| 4-bit Community quantization (estimated) | 14 GB | 16 GB | 22 GB | 30 GB | 20 GB |
Full fine-tuning in mixed precision needs about 407 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 26B A4B on-premise
For a team of 16 using Gemma 4 26B A4B at FP8 with 8K context each (about 34 GB), smallest fit first.
NVIDIA L40S
48 GB per GPU · 9.7 GB to spare.
NVIDIA H100 NVL
94 GB per GPU · 51 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · 53 GB to spare.
NVIDIA DGX Spark
128 GB per system · 75 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · 93 GB to spare.
AMD Instinct MI300X
192 GB per GPU · 139 GB to spare.
AMD Instinct MI325X
256 GB per GPU · 197 GB to spare.
NVIDIA DGX B200
1.44 TB per system · 1.26 TB to spare.
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 26B A4B for your team?
We will size the GPU server for your users and data, and set up Gemma 4 26B A4B on it inside your own building. Free, no obligation.
Gemma 4 26B A4B at a glance
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 26B A4B GPU questions
Can I run Gemma 4 26B locally?
Yes. At 4-bit it needs about 16 GB for one user, so it runs on a single 24 GB-class GPU or larger. A team of 16 fits on NVIDIA L40S.
How much VRAM to run Gemma 4 26B?
Gemma 4 26B A4B needs about 28 GB of GPU memory at FP8 for one user with 8K context: 25 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 34 GB.
Is Gemma 4 26B 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 26B generate images?
No. Gemma 4 26B A4B understands text and images but only produces text.
How big is Gemma 4 26B?
Gemma 4 26B A4B has 25.2B parameters in total, of which 3.8B work on each token. The weights take about 14 GB at 4-bit.
Can Gemma 4 26B run on a single GPU?
Yes. At FP8 with one user and 8K context it needs about 28 GB, which fits on a single NVIDIA L40S or NVIDIA H100 NVL.
Why does Gemma 4 26B need so much memory if only 3.8B parameters are active?
It is a mixture-of-experts model. Only 3.8B parameters work on each token, which keeps it fast, but a different set of experts is picked for every token, so all 25.2B parameters must stay loaded in GPU memory.
Can I fine-tune Gemma 4 26B?
LoRA fine-tuning at FP8 needs roughly 32 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 407 GB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 20.0 KB per token per user. We add about 5% 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. 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 26B A4B 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.