Can I Run Llama 3.3 Nemotron Super 49B V1.5 on a NVIDIA L40?
Runs comfortably at Q6_K — minimal quality loss.
13 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 42.1 GB | 43.6 GB | 40.9 GB | +5.9 GB |
| Q5_1 | 38.4 GB | 39.9 GB | 37.4 GB | +9.6 GB |
| Q5_K_M | 36.4 GB | 37.9 GB | 35.4 GB | +11.6 GB |
| Q5_K_S | 35.4 GB | 36.9 GB | 34.4 GB | +12.6 GB |
| Q5_0 | 35.3 GB | 36.8 GB | 34.3 GB | +12.7 GB |
| Q4_1 | 32.2 GB | 33.7 GB | 31.4 GB | +15.8 GB |
| Q4_K_M | 31.3 GB | 32.8 GB | 30.2 GB | +16.8 GB |
| Q4_K_S | 29.6 GB | 31.1 GB | 28.6 GB | +18.4 GB |
| Q4_0 | 29.1 GB | 30.6 GB | 28.5 GB | +18.9 GB |
| Q3_K_L | 23.2 GB | 24.7 GB | 26.3 GB | +24.8 GB |
| Q3_K_M | 21.9 GB | 23.4 GB | 24.3 GB | +26.1 GB |
| Q3_K_S | 20.2 GB | 21.7 GB | 22.0 GB | +27.8 GB |
| Q2_K | 17.4 GB | 18.9 GB | 18.7 GB | +30.6 GB |
Try it in the cloud first
Don't want to download Llama 3.3 Nemotron Super 49B V1.5 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 48.0GB.
FAQ
Can the NVIDIA L40 run Llama 3.3 Nemotron Super 49B V1.5?
Yes. The NVIDIA L40's 48.0GB of VRAM is enough to run Llama 3.3 Nemotron Super 49B V1.5 at Q6_K quantization (42.1GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 48.0GB. It uses about 42.1GB of memory and 43.6GB 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.