Can I Run DeepSeek R1 0528 Qwen3 8B on a NVIDIA A100 40GB?
Runs at Q8_0 — near-lossless quality.
12 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 9.7 GB | 11.2 GB | 8.7 GB | +30.3 GB |
| Q6_K | 7.8 GB | 9.3 GB | 6.7 GB | +32.3 GB |
| Q5_K_M | 6.8 GB | 8.3 GB | 5.8 GB | +33.2 GB |
| Q5_K_S | 6.7 GB | 8.2 GB | 5.7 GB | +33.3 GB |
| Q4_1 | 6.1 GB | 7.6 GB | 5.3 GB | +33.9 GB |
| Q4_K_M | 6.0 GB | 7.5 GB | 5.0 GB | +34.0 GB |
| Q4_K_S | 5.7 GB | 7.2 GB | 4.8 GB | +34.3 GB |
| Q4_0 | 5.6 GB | 7.1 GB | 4.8 GB | +34.4 GB |
| Q3_K_L | 4.7 GB | 6.2 GB | 4.4 GB | +35.4 GB |
| Q3_K_M | 4.4 GB | 5.9 GB | 4.1 GB | +35.6 GB |
| Q3_K_S | 4.2 GB | 5.7 GB | 3.8 GB | +35.8 GB |
| Q2_K | 3.7 GB | 5.2 GB | 3.3 GB | +36.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 40.0GB.
FAQ
Can the NVIDIA A100 40GB run DeepSeek R1 0528 Qwen3 8B?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run DeepSeek R1 0528 Qwen3 8B at Q8_0 quantization (9.7GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 40.0GB. It uses about 9.7GB of memory and 11.2GB 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.