gemma-4-31B-it-assistant
Gemma 4 31B is Google DeepMind's instruction-tuned dense language model with 30.7 billion parameters. It supports text and image inputs, handles up to 256K token contexts, and includes built-in reasoning modes. Available under Apache 2.0 license and ungated for free use. The 31B variant targets deployment on consumer GPUs and workstations. Multi-Token Prediction (MTP) drafting is available for speculative decoding speedups up to 3x.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | |
| Parameters | 470M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | any-to-any |
| Gated on HuggingFace | No |
| Downloads | 888.9k |
| Likes | 310 |
| Last updated | 2026-06-03 |
| Source | google/gemma-4-31B-it-assistant |
What gemma-4-31B-it-assistant is
A 31-billion parameter dense transformer with hybrid attention (local sliding-window + global), 60 layers, 256K context window, 262K vocabulary. Multimodal (text + image; no audio on 31B). Employs Proportional RoPE for long-context efficiency. Instruction-tuned variant. This checkpoint is the MTP assistant/drafter model for speculative decoding. Benchmarks show strong reasoning (MMLU Pro 85.2%), math (AIME 89.2%), and coding (Codeforces ELO 2150) performance.
Run gemma-4-31B-it-assistant locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="google/gemma-4-31B-it-assistant")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
Estimated 62–124 GB VRAM (fp32–fp16) for full inference; 31–62 GB with bf16. Quantization (int8/int4) can reduce to ~8–16 GB. MTP drafting requires dual-GPU setup (target + assistant) for speculative decoding. Minimum: high-end consumer GPU (H100, RTX 6000) or multi-GPU setup. Cloud inference (TPU/A100 clusters) recommended for production throughput. Verify exact requirements with your deployment framework (vLLM, TGI, etc.).
Card does not mention LoRA, QLoRA, or fine-tuning recipes. Assume full fine-tuning is feasible given Apache 2.0 license. LoRA adaptation likely possible via Hugging Face trainers but not explicitly documented. Instruction-tuned variant suggests it is already optimized for downstream tasks. Test with a small dataset before full fine-tuning due to parameter count.
When to avoid it — and what to weigh
- Real-time, ultra-low-latency inference without GPU — 31B parameters require substantial compute. While MTP drafting aids speed, CPU-only or edge-device deployment will be slow. Use E2B/E4B (2.3B–4.5B effective) for mobile/edge instead.
- Audio processing is required — This 31B variant does not support audio input. E2B and E4B models include native audio encoders; use those if ASR or speech-to-text translation is needed.
- Proprietary, fully closed-source infrastructure — Apache 2.0 license requires attribution. If your policy forbids open-source dependencies or requires complete proprietary stacks, this is unsuitable.
- Safety-critical applications without domain validation — Model card does not detail safety benchmarks, adversarial robustness, or red-teaming results. Evaluate independently for high-stakes domains (medical, legal, financial).
License & commercial use
Apache License 2.0. Requires attribution and permits commercial use, modification, and distribution under the same terms. No special restrictions noted in card.
Apache 2.0 is a permissive OSI-approved license. Commercial use is permitted provided you include a copy of the license and attribute the source. Google DeepMind owns the model weights. No commercial license fee or restricted use clause is stated in the card. Verify compliance with your legal team for liability and trademark implications, but the license itself does not prohibit commercial products.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model card does not disclose security evaluations, adversarial robustness testing, or known jailbreak vectors. As a general-purpose instruction-tuned model, it may generate harmful content if prompted adversarially. Recommended actions: (1) implement input filtering and output validation, (2) test for prompt injection and adversarial inputs, (3) monitor for misuse in production, (4) use with content moderation layers if public-facing. No claims of safety or security posture can be inferred from the card alone.
Alternatives to consider
Gemma 4 26B A4B (Mixture-of-Experts)
Runs ~4x faster (3.8B active parameters) than 31B dense variant while achieving 82.6% MMLU Pro. Better for latency-sensitive applications and resource-constrained environments.
Llama 3.1 70B or 405B (Meta)
Larger parameter count (70B/405B) for frontier-level reasoning and knowledge. Open-source under Llama Community License. More established deployment ecosystem but higher VRAM requirements.
Mistral Large or Mixtral 8x22B
Apache 2.0 licensed, strong coding and reasoning. Mixtral MoE variant offers similar speedup trade-offs as Gemma 4 26B A4B. Smaller VRAM footprint on some quantizations.
Ship gemma-4-31B-it-assistant with senior software developers
Benchmark this model against your reasoning, coding, and long-document tasks. Start with quantized inference (int8/int4) to validate latency and quality on your hardware, then scale with vLLM or TGI. Use MTP speculative decoding for up to 3x speedup on supported frameworks.
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gemma-4-31B-it-assistant FAQ
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Software development & web development with DEV.co
Adopting gemma-4-31B-it-assistant 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 31B?
Benchmark this model against your reasoning, coding, and long-document tasks. Start with quantized inference (int8/int4) to validate latency and quality on your hardware, then scale with vLLM or TGI. Use MTP speculative decoding for up to 3x speedup on supported frameworks.