---
title: "Gemma 4 31B IT | Aguasto"
description: "Research reference for Gemma 4 31B IT: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented."
canonical: "https://www.aguasto.com/models/gemma-4-31b-it"
last-updated: "2026-10-13"
---

# Gemma 4 31B IT | Aguasto

Canonical HTML: [Gemma 4 31B IT](https://www.aguasto.com/models/gemma-4-31b-it)

> Research reference for Gemma 4 31B IT: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented.

[Aguasto](https://www.aguasto.com/) [Model Index](https://www.aguasto.com/models)  Gemma 4 31B IT

[Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools)

Google / Open-weight checkpoint reference

## Gemma 4 31B IT

Documented in Aguasto Research. This checkpoint has not been evaluated or deployed by Aguasto and is not available through an Aguasto API.

Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark.

Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services.

### Checkpoint identity

Official identifier

google/gemma-4-31B-it[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Architecture

Dense multimodal conditional-generation model; hybrid local sliding and global attention[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Total parameters

Not verified for this recordPublished rounded counts; total stored weights determine the preliminary footprint, including all MoE experts.[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Active parameters

Not verified for this recordActive parameters are a compute-related count, not the model's full stored weight count.[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Modalities

text-input, image-input, text-output[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Context

262,144 tokensNative configuration where directly verified. Extensions, advertised K-labels and configuration differences are preserved in the notes below.[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Release date

Exact release date not independently established in the reviewed artifacts[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

Download access

Public checkpoint reference[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

License

Apache 2.0Google documents Apache 2.0 for Gemma 4. These terms differ from the Gemma Terms of Use governing the existing Gemma 3 reference. Preserve applicable license and notice requirements for the exact checkpoint and derivative artifacts.[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

### Theoretical weight-memory footprint

A weight estimate is unavailable without a verified parameter count.

This estimate excludes KV cache, activations, quantization metadata, non-weight buffers and serving overhead. It is not a tested GPU fit. MoE routing activates only part of the network per token while the complete expert weights still need storage.

[Explore the memory estimator](https://www.aguasto.com/tools)

#### Quantization

The main checkpoint configuration declares BF16. Quantized variants need their own artifact provenance and engine support; the 31B name does not specify a storage format or actual runtime-memory requirement.

[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

### Serving considerations

- The publisher's dense-model table reports 30.7B parameters and separately reports an approximately 550M vision encoder. The exact combined artifact boundary is left unknown in the single total field. It is a dense model, not the separate 26B-A4B mixture-of-experts variant.

- The text configuration confirms 262,144 positions. The 31B variant accepts text and images and has no audio encoder; audio capabilities of other Gemma 4 sizes must not be transferred to this checkpoint.

- The hybrid sliding/global attention config, image processing and visual tokens affect cache growth. A full-context estimate based solely on a conventional all-global dense cache should be treated as a preliminary model rather than a measured allocation.

- Google supplies an official model card and documentation. Engine compatibility remains unverified in this record; generic Hugging Face deployment snippets are not evidence of an executed or version-pinned Aguasto environment.

- The profile is a research reference. Aguasto has not tested its GPU fit, long-context quality, memory use or serving throughput and does not claim model availability.

[Google Gemma4 31B IT official model card (external source)](https://huggingface.co/google/gemma-4-31B-it)   · [Gemma4 31B IT checkpoint configuration (external source)](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)   · [Google Gemma4 Apache2.0 license documentation (external source)](https://ai.google.dev/gemma/apache_2)

#### Framework documentation

These are third-party documentation starting points. Support depends on the exact checkpoint, runtime version and hardware.

### Continue the research

[Compare model references](https://www.aguasto.com/models/compare) [GPU specification library](https://www.aguasto.com/gpus) [Weights, cache and serving overhead](https://www.aguasto.com/learn/model-weights-and-runtime-memory)

### Sources & verification

The register preserves each source's publication and verification date. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production.

- [Google Gemma4 31B IT official model card ↗](https://huggingface.co/google/gemma-4-31B-it)

Exact checkpoint identity and openly listed weights; Dense30.7B table boundary and separate~550M vision encoder; Text/image inputs and no31B audio encoder; Hybrid sliding/global attention description. Publication: date not stated in the reviewed source. Verified: 2026-10-13.

- [Gemma4 31B IT checkpoint configuration ↗](https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json)

Gemma4ForConditionalGeneration architecture; BF16 dtype, dense configuration, no audio config; 262144 text max\_position\_embeddings; 1024 sliding window and global/local layer types. Publication: date not stated in the reviewed source. Verified: 2026-10-13.

- [Google Gemma4 Apache2.0 license documentation ↗](https://ai.google.dev/gemma/apache_2)

Google's Gemma4Apache2.0 terms; Distinct licensing from earlier Gemma versions. Publication: date not stated in the reviewed source. Verified: 2026-10-13.

### Reference journey

[Compare models ↗](https://www.aguasto.com/models/compare) [Estimate weights ↗](https://www.aguasto.com/tools) [Hardware references ↗](https://www.aguasto.com/gpus) [Aguasto inference strategy ↗](https://www.aguasto.com/ai-inference) [Sources & verification ↓](https://www.aguasto.com/models/gemma-4-31b-it#sources)

Research reference · No available Aguasto service

## Company navigation

- [Home](https://www.aguasto.com/)
- [Infrastructure](https://www.aguasto.com/infrastructure)
- [GPU Cloud](https://www.aguasto.com/gpu-cloud)
- [AI Inference](https://www.aguasto.com/ai-inference)
- [Development & Investment](https://www.aguasto.com/development)
- [Company](https://www.aguasto.com/company)
- [Contact](https://www.aguasto.com/contact)
- [Privacy](https://www.aguasto.com/privacy)
- [Resources](https://www.aguasto.com/resources)
- [GPU Library](https://www.aguasto.com/gpus)
- [Model Index](https://www.aguasto.com/models)
- [Inference Stack](https://www.aguasto.com/inference-stack)
- [Learn](https://www.aguasto.com/learn)
- [Insights](https://www.aguasto.com/insights)
- [Research](https://www.aguasto.com/research)
- [Industry cases](https://www.aguasto.com/research/cases)
- [Why Modular](https://www.aguasto.com/why-modular)
- [Partners](https://www.aguasto.com/partners)
- [GPU comparison](https://www.aguasto.com/gpus/compare)
- [Model comparison](https://www.aguasto.com/models/compare)
- [Engineering tools](https://www.aguasto.com/tools)
- [Development friction](https://www.aguasto.com/research/hyperscale-development-friction)
- [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100)
- [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200)
- [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200)
- [NVIDIA B300](https://www.aguasto.com/gpus/b300)
- [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72)
- [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72)
- [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72)
- [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x)
- [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x)
- [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x)
- [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x)
- [AMD Helios](https://www.aguasto.com/gpus/helios)
- [GLM-4.5](https://www.aguasto.com/models/glm-4-5)
- [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b)
- [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1)
- [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct)
- [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it)
- [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b)
- [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct)
- [GLM-5.3](https://www.aguasto.com/models/glm-5-3)
- [Qwen3.8-27B](https://www.aguasto.com/models/qwen3-8-27b)
- [DeepSeek-V4.1-Flash](https://www.aguasto.com/models/deepseek-v4-1-flash)
- [Llama 4 Scout 17B-16E Instruct](https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct)
- [Gemma 4 31B IT](https://www.aguasto.com/models/gemma-4-31b-it)
- [Kimi K3](https://www.aguasto.com/models/kimi-k3)
- [vLLM](https://www.aguasto.com/inference-stack/vllm)
- [SGLang](https://www.aguasto.com/inference-stack/sglang)
- [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm)
- [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo)
- [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton)
- [AMD ROCm](https://www.aguasto.com/inference-stack/rocm)
- [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve)
- [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes)
- [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory)
- [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology)
- [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving)
- [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework)
- [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference)
- [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics)
- [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability)
- [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures)
- [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now)
- [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center)
- [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning)
- [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces)
- [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power)
- [What Is KV Cache? Capacity, Reuse and Isolation](https://www.aguasto.com/learn/kv-cache-capacity-and-policy)
- [Continuous Batching: Throughput Without Hiding Latency](https://www.aguasto.com/learn/continuous-batching-and-latency)
- [Applied Digital: Reading the Polaris Forge 1 Delivery Milestones](https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones)
- [Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans](https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion)
- [Official LinkedIn](https://www.linkedin.com/company/aguasto)

This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.