Gemma 4 31B IT — Hardware Requirements & GPU Compatibility
Vision
Gemma 4 31B IT is Google's 31-billion-parameter instruction-tuned model in the Gemma 4 lineup, built to handle both text and image input in a single pass. As a vision-capable model, it can describe, compare, or reason about images alongside written prompts, making it suitable for multimodal chat and document-understanding tasks. At this parameter count, local inference calls for quantization and a fairly capable GPU; it fits on a single high-end consumer or workstation card rather than lower-end hardware. The model supports a 256K token context window, enough for long documents, transcripts, or multi-turn conversations without aggressive truncation. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and reflects Google's continued push toward mid-sized, multimodal open-weight models following the earlier Gemma generations.
9.2M downloads
3.9K likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 31.3B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-03-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Gemma 4 31B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.9 GB | 182.7 GB | 13.29 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.3 GB | 183.1 GB | 13.68 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.9 GB | 184.7 GB | 15.25 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.3 GB | 185.1 GB | 15.64 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.4 GB | 188.2 GB | 18.76 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 23.9 GB | 191.7 GB | 22.28 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.4 GB | 195.2 GB | 25.80 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 32.9 GB | 200.7 GB | 31.27 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 31B IT?
Q4_K_M · 20.4 GBGemma 4 31B IT (Q4_K_M) requires 20.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 262K context window can add up to 167.8 GB, bringing total usage to 188.2 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 4 31B IT?
Q4_K_M · 20.4 GB41 devices with unified memory can run Gemma 4 31B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Gemma 4 31B IT
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Gemma 4 31B IT need?
Gemma 4 31B IT requires 20.4 GB of VRAM at Q4_K_M, or 64.2 GB at BF16. Full 262K context adds up to 167.8 GB (188.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31.3B × 4.8 bits ÷ 8 = 18.8 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 169.4 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M20.4 GBQ4_K_M + full context188.2 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 31B IT?
Yes, at Q5_K_M (23.9 GB) or lower. Higher quantizations like Q5_K_L (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 31B IT?
For Gemma 4 31B IT, Q4_K_M (20.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 10.2 GB.
VRAM requirement by quantization
IQ2_XXS10.2 GBQ2_K14.9 GBIQ4_XS18.4 GBQ4_K_M ★20.4 GBQ4_K_L20.8 GBBF1664.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 31B IT on a Mac?
Gemma 4 31B IT requires at least 10.2 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Gemma 4 31B IT locally?
Yes — Gemma 4 31B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 20.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 31B IT?
At Q4_K_M, Gemma 4 31B IT can reach ~235 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.4 × 0.65 = ~255 tok/s
Estimated speed at Q4_K_M (20.4 GB)
~255 tok/s~32 tok/s~255 tok/s~235 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 4 31B IT?
At Q4_K_M, the download is about 18.76 GB. The full-precision BF16 version is 62.55 GB. The smallest option (IQ2_XXS) is 8.60 GB.
- Which GPUs can run Gemma 4 31B IT?
7 consumer GPUs can run Gemma 4 31B IT at Q4_K_M (20.4 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 31B IT?
41 devices with unified memory can run Gemma 4 31B IT at Q4_K_M (20.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.