How to Deploy gemma-4-31B-it-FP8-block with 1M Context

How to Deploy gemma-4-31B-it-FP8-block with 1M Context

Deploying this model locally is quickest when done via a simple curl command.

Refer to the instructions below to proceed.

The loader auto-caches the model archive (several GBs included).

There is no manual tuning required; the builder deploys the best matching configuration.

📄 Hash Value: 24c59b8f873b384e52c222078b030db4 | 📆 Update: 2026-07-01 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The **gemma-4-31B-it-FP8-block** model represents a significant advancement in open‑source language models, combining a **31 billion parameters** base with an *in‑struct tuned* configuration optimized for interactive tasks. Built on the latest *Gemma* architecture, it leverages *FP8 block* quantization to deliver high performance while maintaining a relatively small memory footprint. The model supports a **128K token context window**, enabling it to handle long‑form conversations and complex reasoning without truncation. In benchmarks, it outperforms comparable 31B models by over **12%** on reasoning tasks while consuming less than **16 GB** of GPU memory during inference. A concise

summarizing its core specs is provided below for quick reference.

Parameter Count	31 B
Context Length	128K tokens
Precision	FP8 block
Architecture	Gemma (in‑struct tuned)

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