Can I Run Qwen3 4B Thinking 2507 on a NVIDIA A100 40GB?
Runs at full precision (fp16). Zero quality loss.
13 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 9.0 GB | 10.5 GB | 8.1 GB | +31.0 GB |
| Q8_0 | 5.3 GB | 6.8 GB | 4.3 GB | +34.8 GB |
| Q6_K | 4.3 GB | 5.8 GB | 3.3 GB | +35.7 GB |
| Q5_K_M | 3.8 GB | 5.3 GB | 2.9 GB | +36.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 2.8 GB | +36.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.6 GB | +36.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.5 GB | +36.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.4 GB | +36.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.4 GB | +36.8 GB |
| Q3_K_L | 2.8 GB | 4.3 GB | 2.2 GB | +37.2 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.1 GB | +37.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.9 GB | +37.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.7 GB | +37.7 GB |
Try it in the cloud first
Don't want to download Qwen3 4B Thinking 2507 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 40.0GB.
FAQ
Can the NVIDIA A100 40GB run Qwen3 4B Thinking 2507?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run Qwen3 4B Thinking 2507 at fp16 quantization (9.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 40.0GB. It uses about 9.0GB of memory and 10.5GB 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.