Can I Run / Gemma 4 26B A4B / on NVIDIA RTX 6000 Ada
Can I Run Gemma 4 26B A4B on a NVIDIA RTX 6000 Ada?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
26.5B
GPU memory
48.0GB
Smallest quant
Q2_K
Best fit
Q8_0
10 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 |
| Q4_0 | 15.9 GB | 17.4 GB | 14.6 GB | +32.1 GB |
| Q3_K_L | 12.8 GB | 14.3 GB | 13.8 GB | +35.2 GB |
| Q3_K_M | 12.1 GB | 13.6 GB | 12.7 GB | +35.9 GB |
| Q2_K | 9.7 GB | 11.2 GB | 10.6 GB | +38.3 GB |
Try it in the cloud first
Don't want to download Gemma 4 26B A4B just to try it? Use a hosted API or rent a GPU by the second.
Affiliate links — we earn a commission at no cost to you.
Advertisement
Full model details
Gemma 4 26B A4B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 6000 Ada →
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX 6000 Ada run Gemma 4 26B A4B?
Yes. The NVIDIA RTX 6000 Ada's 48.0GB of VRAM is enough to run Gemma 4 26B A4B 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.