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
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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. 8 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 GB41 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 headroomDecent
— Enough memory, may be tightWhere 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.
Related Models
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 ~233 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 18.9 × 0.65 = ~275 tok/s
Estimated speed at Q4_K_M (18.9 GB)
~275 tok/s~35 tok/s~275 tok/s~233 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?
8 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?
41 devices with unified memory can run Gemma 4 31B IT Qat Q4 0 Unquantized Assistant at Q4_K_M (18.9 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.