Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant — Hardware Requirements & GPU Compatibility
Vision
Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant is a 26B-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 15.92 GB of VRAM — see which GPUs and Macs can run it below.
611 downloads
8 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 26B
- 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 26B A4B 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 | 11.4 GB | 12.4 GB | 11.05 GB | 2-bit quantization with K-quant improvements |
| IQ3_M | 3.60 | 12.0 GB | 13.1 GB | 11.70 GB | Importance-weighted 3-bit, medium |
| Q3_K_Mest. | 3.90 | 13.0 GB | 14.1 GB | 12.68 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 13.3 GB | 14.4 GB | 13.00 GB | 4-bit legacy quantization |
| IQ4_NL | 4.50 | 14.9 GB | 16 GB | 14.63 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_Mest. | 4.80 | 15.9 GB | 17.0 GB | 15.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 18.8 GB | 19.9 GB | 18.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 21.8 GB | 22.8 GB | 21.45 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 26.3 GB | 27.4 GB | 26.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 52.3 GB | 53.4 GB | 52.00 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 26B A4B IT Qat Q4 0 Unquantized Assistant?
Q4_K_M · 15.9 GBGemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant (Q4_K_M) requires 15.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 131K context window can add up to 1.0 GB, bringing total usage to 17.0 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
Q4_K_M · 15.9 GB47 devices with unified memory can run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download Gemma 4 26B A4B 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 26B A4B IT Qat Q4 0 Unquantized Assistant need?
Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant requires 15.9 GB of VRAM at Q4_K_M, or 52.3 GB at BF16. Full 131K context adds up to 1.0 GB (17.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 26B × 4.8 bits ÷ 8 = 15.6 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_M15.9 GBQ4_K_M + full context17.0 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
Yes, at Q6_K (21.8 GB) or lower. Higher quantizations like Q8_0 (26.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
For Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant, Q4_K_M (15.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (18.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.4 GB.
VRAM requirement by quantization
Q2_K11.4 GBQ3_K_M13.0 GBQ4_K_M ★15.9 GBQ5_K_M18.8 GBQ6_K21.8 GBBF1652.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant on a Mac?
Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant requires at least 11.4 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 26B A4B IT Qat Q4 0 Unquantized Assistant locally?
Yes — Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant can run locally on consumer hardware. At Q4_K_M quantization it needs 15.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
At Q4_K_M, Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant can reach ~763 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~253 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 ÷ 15.9 × 0.65 = ~1447 tok/s
Estimated speed at Q4_K_M (15.9 GB)
~1447 tok/s~253 tok/s~1447 tok/s~1011 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 26B A4B IT Qat Q4 0 Unquantized Assistant?
At Q4_K_M, the download is about 15.60 GB. The full-precision BF16 version is 52.00 GB. The smallest option (Q2_K) is 11.05 GB.
- Which GPUs can run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
26 consumer GPUs can run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant at Q4_K_M (15.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant?
49 devices with unified memory can run Gemma 4 26B A4B IT Qat Q4 0 Unquantized Assistant at Q4_K_M (15.9 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.