Open-Weight LLM · Private & Custom AI
gemma-4-31B-it-NVFP4-turbo
A quantized 31B instruction-tuned LLM optimized for high-throughput, low-latency private deployment on Blackwell GPUs—built for ops teams automating workflows at scale without leaving your infrastructure.
Gemma 4 31B IT NVFP4 Turbo is a 4-bit quantized derivative of Google's Gemma 4 31B instruction-tuned model, stripped of vision/audio encoders and optimized for NVIDIA Blackwell tensor cores. It trades 1–3% accuracy loss for 68% memory savings and ~2.5× throughput gains versus the base model. For ops teams running private LLM infrastructure, this means fitting a capable reasoning model on a single RTX 5090 while handling 6+ concurrent requests—critical for document processing, support ticket triage, and agent-based workflow automation.
Model facts
Private deployment
Run gemma-4-31B-it-NVFP4-turbo in your own environment
Deploy via vLLM + Docker (CUDA 13.0) or bare-metal pip install on any Blackwell GPU (RTX 5090 minimum; RTX PRO 6000 / B200 for higher concurrency). The model requires `--quantization modelopt` to activate FP4 kernel paths—without it, performance degrades significantly. Self-hosting keeps all customer data, request logs, and fine-tuning artifacts in your own environment; no external API calls, no vendor lock-in, full audit trail. Budget ~18.5 GiB VRAM with FP8 KV cache, or ~20 GiB for headroom. Context window scales to ~25K on RTX 5090, ~180K on PRO 6000.
Operational AI use cases
Support Ticket Classification & Triage
Ingest incoming support tickets (email, chat, form submissions) into a private vLLM instance. Route tickets by urgency, category, and required skill—hand off to routing queues or trigger auto-responses. Model fits in 18.5 GiB; ~15K tokens/sec prefill means 1K-token tickets processed in <100ms per batch. No customer data leaves your infrastructure.
Finance & Compliance Document Processing
Parse invoices, contracts, compliance reports using batch inference. Extract entities (vendor, amount, dates, terms) and flag anomalies (e.g., invoice amount vs. PO mismatch). Run nightly batch jobs at 1,244 tok/sec (batched decode); sensitive financial data remains on-premises. ~10 concurrent requests per RTX 5090 for document workflows.
Internal Knowledge Base Q&A & Search
Index internal wikis, runbooks, and SOPs as embeddings (via external embedding model), then use Gemma 4 31B Turbo as a retrieval-augmented generation (RAG) backbone to answer employee questions. Deploy as an internal chatbot; all queries and knowledge assets stay private. Concurrent throughput means 50–100 employees hitting the system simultaneously without bottleneck.
Custom AI
As a base for custom AI
Gemma 4 31B is instruction-tuned and chat-ready, making it a strong foundation for custom ops AI applications. Fine-tune on your domain (e.g., internal policy Q&A, ticket resolution playbooks, financial analysis frameworks) using LoRA or full-parameter methods; the small footprint leaves room for adapters on a single GPU. Use as the backbone for agentic workflows—attach tool-calling logic to call your CRM, ticket system, or knowledge base. Quantization locks performance gains, so reserve fine-tuning for critical custom behaviors rather than general capability expansion.
In the operating system
Where it fits
In an LLM.co-style ops AI stack, this model serves as the **core reasoning layer** in the workflow execution tier. It bridges the knowledge retrieval layer (embeddings, vector search) and the agent layer (tool-calling, state management). For companies building internal copilots, ticket automation, or document intelligence systems, Gemma 4 31B Turbo is the inference backbone—fast enough for real-time request/response loops, private enough for regulated workflows, and efficient enough to run multiple instances on modest hardware.
Data control & security
Self-hosting on your own Blackwell GPU means zero external inference calls—customer data, internal documents, and request metadata remain in your data center. No API logs on third-party servers, no risk of model weights leaking into public training sets, no dependency on vendor availability. **Important caveat:** quantization and model behavior are not inherently "secure" or "compliant." You remain responsible for input validation, output filtering, and access controls. Use standard practices: encrypt data in transit (TLS), at rest (disk encryption), isolate the vLLM instance on a private network, and implement request authentication. No guarantees about model hallucinations, prompt injection, or adversarial robustness—treat it like any other LLM layer in your security posture.
Hardware footprint
**Estimate (verify in your environment):** ~18.5 GiB VRAM (FP4 weights + FP8 KV cache at max context 16K). Without KV cache optimization, budget ~20–22 GiB. Requires NVIDIA Blackwell GPU (RTX 5090 with 32 GiB, RTX PRO 6000 with 96 GiB, or B200/B100 datacenter chips). Older GPUs (H100, A100, RTX 4090) lack FP4 tensor cores; model may load but lose optimized kernels, degrading throughput by 50–70%. CPU footprint: modest (~8–16 GiB for vLLM runtime + OS). Network: streaming inference produces ~1–10 MB/sec egress per concurrent user depending on output token rate.
Integration
Expose vLLM via OpenAI-compatible REST API (`/v1/chat/completions`). Drop into any LLM framework that supports OpenAI-style endpoints: LangChain, LlamaIndex, Haystack, or custom orchestration. Wire it into your ops stack via direct HTTP calls or SDKs (Python, Node.js, Go). For agent workflows, attach function-calling via OpenAI-format `tools` parameter (requires testing—Gemma 4 supports tool-calling but behavior varies by fine-tune/prompt engineering). Stream responses to reduce latency perception. Monitor vLLM logs and Prometheus metrics (concurrency, latency, token throughput) to track utilization and cost per request.
When it's not the right fit
- —You need sub-50ms latency for single-request inference—Gemma 4 31B decode achieves ~51 tok/sec, meaning time-to-first-token is 70–100ms, suitable for batch workflows but not real-time interactive chat at scale.
- —Your use case demands very long contexts (>180K tokens) on consumer GPUs—the model's max practical context on RTX 5090 is ~25K; larger windows require RTX PRO 6000 or B200.
- —You require guaranteed factuality, reasoning transparency, or adversarial robustness—Gemma 4 31B is a general-purpose LLM prone to hallucinations and prompt injection, not a specialist reasoning engine or safe decision-making system.
- —Your infrastructure is CPU-only or AMD—this quantization is tightly coupled to NVIDIA Blackwell FP4 cores; AMD MI300X or older NVIDIA GPUs cannot unlock the performance claims.
Alternatives to consider
Llama 3.1 70B (Meta)
Larger, more capable, but requires 2–3 GPUs or heavy quantization; less ops-optimized than Gemma 4 31B Turbo. Better for complex reasoning, worse for single-GPU deployment.
Mistral 7B / 12B (Mistral AI)
Smaller, faster, fits on RTX 4090 and older GPUs. Good for latency-sensitive ops (support triage, classification), but less instruction-tuning means more prompt engineering for complex workflows.
Qwen 2.5 32B (Alibaba, quantized)
Similar size and performance tier; supports more languages and longer context out-of-box. Not Blackwell-optimized, so throughput parity or slower depending on quantization method.
Related open models
FAQ
Can I run this model on my existing infrastructure (RTX 4090, A100)?
Technically yes—the model will load—but you'll lose the Blackwell FP4 tensor core optimizations. Throughput will drop to ~6–7K tok/sec prefill (vs. 15K) and ~40–50 tok/sec batched decode (vs. 1.2K), making concurrent throughput roughly 2–3x slower. You're better off using a smaller quantization (AWQ, GPTQ) or a smaller model.
Is this commercially usable? Can I sell a product using this model?
Yes—Apache 2.0 license permits commercial use, redistribution, and derivative works without royalties or vendor approval. You may package it into a product, charge for inference services, and fine-tune it. **Note:** you must include the Apache 2.0 license text and any notices from NVIDIA's original Gemma 4 quantization. Verify compliance with your legal team, especially if integrating third-party dependencies (vLLM, transformers library).
How do I measure whether this model is meeting my ops requirements?
Benchmark on your actual workload: run a sample of your support tickets, documents, or queries through vLLM with production concurrency settings. Log latency (TTFT and token decode rate), throughput (tokens/sec), accuracy/quality (manual spot-check or auto-evals against gold labels), and utilization (GPU memory, CPU load). Compare against the numbers in the model card as a sanity check; your mileage will vary with context length, batch size, and hardware.
Can I fine-tune this model for my company's specific ops use cases?
Yes—use LoRA (low-rank adaptation) or full fine-tuning on your own GPU cluster. Start with LoRA (~2 GiB overhead) for task-specific behavior (e.g., ticket classification, jargon learning). Apache 2.0 permits this without restriction. **Caveat:** fine-tuning on quantized weights is less common; you may need to dequantize to FP16, fine-tune, then re-quantize, or use quantization-aware training libraries (PEFT, bitsandbytes). Test thoroughly before production.
Build Your Private Ops AI Stack
Gemma 4 31B Turbo is powerful, but running it effectively requires orchestration: connecting it to your ticket systems, documents, and agents. LLM.co helps mid-market companies build end-to-end ops AI systems—from quantization and vLLM deployment to RAG pipelines, agent workflows, and operational integrations. Let's architect your private LLM infrastructure.