gemma-4-31B-it-NVFP4-turbo
Gemma 4 31B IT NVFP4 Turbo is a quantized variant of Google's Gemma 4 31B instruction-tuned model, optimized for NVIDIA Blackwell GPUs (RTX 5090, RTX PRO 6000, B200). It reduces memory footprint to 18.5 GB (68% smaller than base) and achieves ~2.5× faster inference than the unquantized model while maintaining 97–99% of the original quality. The model is text-only and uses 4-bit FP4 quantization with NVIDIA's ModelOpt framework.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | LilaRest |
| Parameters | 32.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 189.1k |
| Likes | 298 |
| Last updated | 2026-04-10 |
| Source | LilaRest/gemma-4-31B-it-NVFP4-turbo |
What gemma-4-31B-it-NVFP4-turbo is
This is a repackaged quantization of nvidia/Gemma-4-31B-IT-NVFP4, applying additional quantization to self-attention weights (FP4, group_size=16) while preserving MLP calibration and embeddings. Compiled for vLLM ≥0.19 with CUDA 13.0. Requires `--quantization modelopt` flag to activate CUTLASS kernels. Supports prefix caching and fp8 KV cache. Maximum context ~25K on RTX 5090, ~180K on RTX PRO 6000. Benchmarks show prefill 15,359 tok/s, decode 1,244 tok/s (batched), and ~6.2 concurrent requests/s on RTX PRO 6000 at 1K input / 200 output token workload.
Run gemma-4-31B-it-NVFP4-turbo locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="LilaRest/gemma-4-31B-it-NVFP4-turbo")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Minimum 18.5 GB VRAM (RTX 5090 primary target, 32 GB VRAM). RTX PRO 6000 (96 GB) and B200/B100 (192 GB) also supported. CUDA 13.0 required for optimal FP4 tensor core acceleration. Requires transformers ≥5.5.0 and vLLM ≥0.19. RTX 5080 and lower GPUs have insufficient VRAM. Note: These are model card estimates; verify against your hardware before deployment.
Unknown. Model card does not document LoRA, QLoRA, or full fine-tuning feasibility on quantized weights. Quantization may prevent standard fine-tuning; consider the base google/gemma-4-31B-it for training workflows.
When to avoid it — and what to weigh
- Need to run on non-Blackwell GPUs with full performance — Model requires SM 12.0+ for optimized FP4 tensor core kernels. Older GPUs (H100, A100, RTX 4090) will run without ModelOpt, but performance degrades significantly (no FP4 acceleration).
- Multimodal inference (video/audio required) — This variant is text-only; vision and audio encoders have been stripped. If you need video/audio support, use the base model or open an issue with the maintainer.
- Offline/edge deployment without CUDA 13.0 infrastructure — Requires vLLM with CUDA 13.0 and Blackwell-compatible hardware. No llama.cpp or lightweight inference support documented. Standard pip vLLM ships CUDA 12, which does not support the required kernel path.
- Fine-tuning workflows — Quantized model is optimized for inference only. Fine-tuning notes are not provided; feasibility is Unknown. Consider starting with the base model if training is required.
License & commercial use
Licensed under Apache License 2.0 (apache-2.0). This is a permissive OSI-approved license. Model is not gated; publicly accessible without authentication.
Apache 2.0 is a permissive open-source license that permits commercial use, modification, and distribution, subject to license attribution and liability disclaimers. However, the base model is Google's Gemma 4, which is subject to Google's Gemma Terms of Use. Verify Google's Gemma licensing terms separately before commercial deployment, as they may impose additional restrictions not covered by this quantization's Apache 2.0 license alone. Requires review before production commercial use.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model card does not discuss adversarial robustness, prompt injection, or data poisoning mitigations. Quantization may alter model behavior in edge cases; validate against your threat model before production. Requires HuggingFace remote code execution (--trust-remote-code flag); verify the model repo and quantization config before deployment. Standard transformer model risks apply (e.g., bias in training data, hallucination potential).
Alternatives to consider
nvidia/Gemma-4-31B-IT-NVFP4 (parent quantization)
Uses same FP4 quantization and NVIDIA ModelOpt but retains ~31 GB VRAM footprint. Better if you prioritize maximum quality over memory efficiency and have the extra 12.5 GB VRAM headroom.
prithivMLmods/gemma-4-31B-it-NVFP4
Similar memory footprint (19.6 GB) but uses kernel paths that do not leverage Blackwell FP4 cores, resulting in 2.3× lower concurrent throughput (3.79 req/s vs. 6.22 req/s). Prefer Turbo for throughput-sensitive workloads.
google/gemma-4-31B-it (base model)
Unquantized, ~59 GB VRAM. Choose if you need fine-tuning, full multimodal support (audio/video), or have sufficient GPU memory and prioritize maximum quality.
Ship gemma-4-31B-it-NVFP4-turbo with senior software developers
Evaluate hardware requirements (Blackwell GPU, CUDA 13.0) and verify Gemma licensing compliance. Use our Docker quick-start or pip installation guide to benchmark on your infrastructure.
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gemma-4-31B-it-NVFP4-turbo FAQ
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Software developers & web developers for hire
Adopting gemma-4-31B-it-NVFP4-turbo is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to deploy Gemma 4 NVFP4 Turbo?
Evaluate hardware requirements (Blackwell GPU, CUDA 13.0) and verify Gemma licensing compliance. Use our Docker quick-start or pip installation guide to benchmark on your infrastructure.