Can I Run Gemma 4 26B A4B (free) on a NVIDIA H100 NVL?
Runs at full precision (fp16). Zero quality loss.
8 quantizations fit your 94.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 54.0 GB | 55.5 GB | 0.9 GB | +40.0 GB |
| Q8_0 | 29.2 GB | 30.7 GB | 0.5 GB | +64.8 GB |
| Q6_K | 22.8 GB | 24.3 GB | 23.2 GB | +71.2 GB |
| Q5_K_M | 19.8 GB | 21.3 GB | 21.1 GB | +74.2 GB |
| Q5_K_S | 19.3 GB | 20.8 GB | 18.9 GB | +74.7 GB |
| Q4_K_M | 17.1 GB | 18.6 GB | 16.9 GB | +76.9 GB |
| Q4_K_S | 16.2 GB | 17.7 GB | 16.5 GB | +77.8 GB |
| Q3_K_M | 12.1 GB | 13.6 GB | 12.7 GB | +81.9 GB |
Try it in the cloud first
Don't want to download Gemma 4 26B A4B (free) 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.
All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 94.0GB.
FAQ
Can the NVIDIA H100 NVL run Gemma 4 26B A4B (free)?
Yes. The NVIDIA H100 NVL's 94.0GB of VRAM is enough to run Gemma 4 26B A4B (free) at fp16 quantization (54.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 94.0GB. It uses about 54.0GB of memory and 55.5GB 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.