A100 40GB SXM4 has 40 GB of VRAM and 1,550 GB/s of memory bandwidth, plus 600 GB/s of NVLink. It runs 20 models from the catalog on a single card — up to Qwen 3 30B-A3B (MoE) — and serves Llama 3.3 70B at 3493 tok/s aggregate. All figures are computed by tokcalc's open-source capacity model.
| Vendor | NVIDIA |
| Category | datacenter |
| VRAM | 40 GB |
| Memory bandwidth | 1,550 GB/s |
| FP16/BF16 dense compute | 312 TFLOPS |
| NVLink bandwidth | 600 GB/s |
| Release year | 2020 |
| Typical cloud price | $0.9/hr |
| Model | Params | Quant | VRAM needed | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|---|---|
| GTE Large (1024-dim) | 0.44B | fp16 | 7.32 GB | 13739 | $0.0182 |
| Multilingual E5 Large (1024-dim) | 0.56B | fp16 | 7.56 GB | 10795 | $0.0232 |
| BGE-M3 (embed, 1024-dim) | 0.57B | fp16 | 7.58 GB | 10643 | $0.0235 |
| SmolLM2 1.7B Instruct | 1.70B | fp16 | 16.28 GB | 3556 | $0.0703 |
| Phi-3 Mini 3.8B | 3.80B | fp16 | 33.37 GB | 1591 | $0.1572 |
| Qwen 3 4B | 4B | fp16 | 17.66 GB | 1511 | $0.1654 |
| Qwen 2.5-VL 7B (VLM) | 7B | fp16 | 17.76 GB | 863.6 | $0.2895 |
| Mistral 7B v0.3 | 7.24B | fp16 | 23.07 GB | 834.9 | $0.2994 |
| Qwen2 7B | 7.62B | fp16 | 19.00 GB | 793.3 | $0.3151 |
| Qwen 3 8B | 8B | fp16 | 25.66 GB | 755.6 | $0.3309 |
| Llama 3 8B | 8.03B | fp16 | 24.65 GB | 752.8 | $0.3321 |
| Falcon 3 10B Instruct | 10B | fp16 | 30.74 GB | 604.5 | $0.4136 |
| Pixtral 12B (VLM) | 12B | fp16 | 34.74 GB | 503.8 | $0.4963 |
| OLMo 2 13B | 13B | fp16 | 36.74 GB | 465.0 | $0.5376 |
| Phi-4 14B | 14B | fp16 | 38.74 GB | 431.8 | $0.5790 |
| Qwen 3 14B | 14B | fp16 | 38.74 GB | 431.8 | $0.5790 |
| GPT-OSS 20B (MoE) | 20.91B | fp8 | 22.55 GB | 5038 | $0.0496 |
| Codestral 25.08 | 22B | fp8 | 37.03 GB | 824.3 | $0.3033 |
| Qwen3.8 27B (VLM) | 27.78B | fp8 | 33.28 GB | 652.8 | $0.3830 |
| Qwen 3 30B-A3B (MoE) | 30B | fp8 | 36.44 GB | 6045 | $0.0414 |
| GPU | Aggregate tok/s | Cost / 1M tok | $/hr |
|---|---|---|---|
| Cerebras CS-3 (WSE-3) | 45076 | — | — |
| RTX 4080 16GB | 6464 | $0.1031 | $0.3 |
| RTX A4000 16GB | 4039 | $0.1651 | $0.3 |
| RTX 5090 32GB | 4039 | $0.1376 | $0.5 |
| A100 40GB SXM4 | 3493 | $0.2863 | $0.9 |
| RTX 4090 24GB | 2272 | $0.1956 | $0.4 |
| V100 SXM2 32GB | 2028 | $0.2739 | $0.5 |
| RTX 3090 24GB | 2017 | $0.1377 | $0.25 |
| H100 SXM5 80GB | 1888 | $0.7358 | $2.5 |
| TPU v6e Trillium (32GB) | 1846 | $0.4816 | $0.8 |
| T4 16GB | 1836 | $0.2421 | $0.2 |
| A10G 24GB | 1352 | $0.2958 | $0.36 |
A100 40GB SXM4 is a datacenter accelerator from NVIDIA with 40 GB of VRAM and 1,550 GB/s of memory bandwidth and 600 GB/s of NVLink, typically around $0.9/hr on cloud rental. It is aimed at production LLM serving.
20 models in the tokcalc catalog fit on one A100 40GB SXM4, up to Qwen 3 30B-A3B (MoE) (30B params) at fp8. Below that, throughput and cost per million tokens improve as the model gets smaller.
At fp8 with 8 concurrent users, 4× A100 40GB SXM4 serves 3493 tokens/sec aggregate (72.78 tok/s per stream), with a 1.90 s time to first token. That works out to $0.2863 per 1M output tokens.
Around $0.9/hr for on-demand cloud rental (a typical 2025–2026 estimate across RunPod, Lambda, Modal, AWS, GCP and similar providers). Actual pricing varies by region, commitment, and spot availability — the Build vs Buy tab in the calculator pulls live quotes from several providers.
Memory bandwidth drives decode speed and VRAM drives capacity. A100 40GB SXM4 has 1,550 GB/s of bandwidth and 40 GB of VRAM, plus 312 TFLOPS of dense FP16/BF16 compute. The comparison table on this page ranks every accelerator in the catalog on the same reference workload so you can see where it lands.
Pick your model, context length, and concurrency — the calculator computes the rest with the same formulas.