Gemma 4 31B IT DFlash — Hardware Requirements & GPU Compatibility
Chat
Gemma 4 31B IT DFlash is a 1.5B-parameter open language model from z-lab in the Gemma family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 1.25 GB of VRAM — see which GPUs and Macs can run it below.
35.3K downloads
94 likesSpecifications
- Publisher
- z-lab
- Family
- Gemma
- Parameters
- 1.5B
- Architecture
- DFlashDraftModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-05-08
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Gemma 4 31B IT DFlash Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 1.3 GB | 4.7 GB | 0.92 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 1.6 GB | 5.1 GB | 1.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | 5.4 GB | 1.54 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 31B IT DFlash?
Q4_K_M · 1.3 GBGemma 4 31B IT DFlash (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 3.5 GB, bringing total usage to 4.7 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 4 31B IT DFlash?
Q4_K_M · 1.3 GB33 devices with unified memory can run Gemma 4 31B IT DFlash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 4 31B IT DFlash need?
Gemma 4 31B IT DFlash requires 1.3 GB of VRAM at Q4_K_M, or 1.9 GB at Q8_0. Full 262K context adds up to 3.5 GB (4.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M1.3 GBQ4_K_M + full context4.7 GB- What's the best quantization for Gemma 4 31B IT DFlash?
For Gemma 4 31B IT DFlash, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q6_K (1.6 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★1.3 GBQ6_K1.6 GBQ8_01.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 31B IT DFlash on a Mac?
Gemma 4 31B IT DFlash requires at least 1.3 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 DFlash locally?
Yes — Gemma 4 31B IT DFlash can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 31B IT DFlash?
At Q4_K_M, Gemma 4 31B IT DFlash can reach ~2332 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~524 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 ÷ 1.3 × 0.55 = ~2332 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~2332 tok/s~524 tok/s~1743 tok/s~1442 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 DFlash?
At Q4_K_M, the download is about 0.92 GB. The full-precision Q8_0 version is 1.54 GB.
- Which GPUs can run Gemma 4 31B IT DFlash?
35 consumer GPUs can run Gemma 4 31B IT DFlash at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 31B IT DFlash?
33 devices with unified memory can run Gemma 4 31B IT DFlash at Q4_K_M (1.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.