Can I Run Gemma 4 26B A4B (free) on a NVIDIA RTX A6000?
Runs at Q8_0 — near-lossless quality.
7 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 29.2 GB | 30.7 GB | 0.5 GB | +18.8 GB |
| Q6_K | 22.8 GB | 24.3 GB | 23.2 GB | +25.2 GB |
| Q5_K_M | 19.8 GB | 21.3 GB | 21.1 GB | +28.2 GB |
| Q5_K_S | 19.3 GB | 20.8 GB | 18.9 GB | +28.7 GB |
| Q4_K_M | 17.1 GB | 18.6 GB | 16.9 GB | +30.9 GB |
| Q4_K_S | 16.2 GB | 17.7 GB | 16.5 GB | +31.8 GB |
| Q3_K_M | 12.1 GB | 13.6 GB | 12.7 GB | +35.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX A6000 run Gemma 4 26B A4B (free)?
Yes. The NVIDIA RTX A6000's 48.0GB of VRAM is enough to run Gemma 4 26B A4B (free) at Q8_0 quantization (29.2GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 48.0GB. It uses about 29.2GB of memory and 30.7GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.