Gemma 4 31B IT — Hardware Requirements & GPU Compatibility
Vision
Gemma 4 31B IT is a 32.7B-parameter open language model from Google in the Gemma family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 21.23 GB of VRAM — see which GPUs and Macs can run it below.
11.1M downloads
2.9K likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 32.7B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- 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 |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 21.2 GB | 189.0 GB | 19.61 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 28.6 GB | 196.4 GB | 26.96 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 34.3 GB | 202.1 GB | 32.68 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 31B IT?
Q4_K_M · 21.2 GBGemma 4 31B IT (Q4_K_M) requires 21.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 262K context window can add up to 167.8 GB, bringing total usage to 189.0 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 4 31B IT?
Q4_K_M · 21.2 GB21 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 headroomBenchmarks
View all 4 →Related Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Gemma 4 31B IT need?
Gemma 4 31B IT requires 21.2 GB of VRAM at Q4_K_M, or 34.3 GB at Q8_0. Full 262K context adds up to 167.8 GB (189.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.7B × 4.8 bits ÷ 8 = 19.6 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_M21.2 GBQ4_K_M + full context189.0 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 31B IT?
Yes, at Q4_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (28.6 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 (21.2 GB) offers the best balance of quality and VRAM usage. Q6_K (28.6 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★21.2 GBQ6_K28.6 GBQ8_034.3 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 21.2 GB at Q4_K_M, 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 21.2 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 ~137 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~31 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 21.2 × 0.55 = ~137 tok/s
Estimated speed at Q4_K_M (21.2 GB)
~137 tok/s~31 tok/s~103 tok/s~85 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 19.61 GB. The full-precision Q8_0 version is 32.68 GB.
- Which GPUs can run Gemma 4 31B IT?
5 consumer GPUs can run Gemma 4 31B IT at Q4_K_M (21.2 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?
21 devices with unified memory can run Gemma 4 31B IT at Q4_K_M (21.2 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.