Gemma 4 31B IT Qat Q4 0 Unquantized Assistant — Hardware Requirements & GPU Compatibility
Vision
Gemma 4 31B IT Qat Q4 0 Unquantized Assistant is a 31B-parameter open language model from Google in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 18.92 GB of VRAM — see which GPUs and Macs can run it below.
16.0K downloads
14 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 31B
- Architecture
- Gemma4AssistantForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-05-29
- License
- Apache 2.0
Get Started
How Much VRAM Does Gemma 4 31B IT Qat Q4 0 Unquantized Assistant Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 13.5 GB | 14.6 GB | 13.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 15.4 GB | 16.5 GB | 15.11 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 15.8 GB | 16.9 GB | 15.50 GB | 4-bit legacy quantization |
| Q4_K_Mest. | 4.80 | 18.9 GB | 20.0 GB | 18.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 22.4 GB | 23.5 GB | 22.09 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 25.9 GB | 26.9 GB | 25.57 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 31.3 GB | 32.4 GB | 31.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 62.3 GB | 63.4 GB | 62.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
Q4_K_M · 18.9 GBGemma 4 31B IT Qat Q4 0 Unquantized Assistant (Q4_K_M) requires 18.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 131K context window can add up to 1.0 GB, bringing total usage to 20.0 GB. 6 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 Qat Q4 0 Unquantized Assistant?
Q4_K_M · 18.9 GB21 devices with unified memory can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomWhere to Download Gemma 4 31B IT Qat Q4 0 Unquantized Assistant
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Gemma 4 31B IT Qat Q4 0 Unquantized Assistant need?
Gemma 4 31B IT Qat Q4 0 Unquantized Assistant requires 18.9 GB of VRAM at Q4_K_M, or 62.3 GB at BF16. Full 131K context adds up to 1.0 GB (20.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31B × 4.8 bits ÷ 8 = 18.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M18.9 GBQ4_K_M + full context20.0 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
Yes, at Q5_K_M (22.4 GB) or lower. Higher quantizations like Q6_K (25.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
For Gemma 4 31B IT Qat Q4 0 Unquantized Assistant, Q4_K_M (18.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (22.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 13.5 GB.
VRAM requirement by quantization
Q2_K13.5 GBQ4_015.8 GBQ4_K_M ★18.9 GBQ5_K_M22.4 GBQ6_K25.9 GBBF1662.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant on a Mac?
Gemma 4 31B IT Qat Q4 0 Unquantized Assistant requires at least 13.5 GB at Q2_K, 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 Qat Q4 0 Unquantized Assistant locally?
Yes — Gemma 4 31B IT Qat Q4 0 Unquantized Assistant can run locally on consumer hardware. At Q4_K_M quantization it needs 18.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
At Q4_K_M, Gemma 4 31B IT Qat Q4 0 Unquantized Assistant can reach ~154 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~35 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 ÷ 18.9 × 0.55 = ~154 tok/s
Estimated speed at Q4_K_M (18.9 GB)
~154 tok/s~35 tok/s~115 tok/s~95 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 Qat Q4 0 Unquantized Assistant?
At Q4_K_M, the download is about 18.60 GB. The full-precision BF16 version is 62.00 GB. The smallest option (Q2_K) is 13.18 GB.
- Which GPUs can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
6 consumer GPUs can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant at Q4_K_M (18.9 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant?
21 devices with unified memory can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant at Q4_K_M (18.9 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.