Can I Run Gemma 4 26B A4B (free) on a NVIDIA B200?
Runs at full precision (f32). Zero quality loss.
9 quantizations fit your 192GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 107.0 GB | 108.5 GB | 2.3 GB | +85.0 GB |
| fp16 | 54.0 GB | 55.5 GB | 0.9 GB | +138.0 GB |
| Q8_0 | 29.2 GB | 30.7 GB | 0.5 GB | +162.8 GB |
| Q6_K | 22.8 GB | 24.3 GB | 23.2 GB | +169.2 GB |
| Q5_K_M | 19.8 GB | 21.3 GB | 21.1 GB | +172.2 GB |
| Q5_K_S | 19.3 GB | 20.8 GB | 18.9 GB | +172.7 GB |
| Q4_K_M | 17.1 GB | 18.6 GB | 16.9 GB | +174.9 GB |
| Q4_K_S | 16.2 GB | 17.7 GB | 16.5 GB | +175.8 GB |
| Q3_K_M | 12.1 GB | 13.6 GB | 12.7 GB | +179.9 GB |
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Top-ranked open-source models that fit in 192GB.
FAQ
Can the NVIDIA B200 run Gemma 4 26B A4B (free)?
Yes. The NVIDIA B200's 192GB of VRAM is enough to run Gemma 4 26B A4B (free) at f32 quantization (107.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 192GB. It uses about 107.0GB of memory and 108.5GB 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.