NVIDIA A100 80GB vs NVIDIA L40S
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
NVIDIA A100 80GB wins for local AI inference. It has 32 GB more VRAM and 136% more memory bandwidth, runs 54 models natively (vs 53), and exclusively fits 1 models the other cannot.
Specs comparison
| Spec | NVIDIA A100 80GB | NVIDIA L40S |
|---|---|---|
| VRAM | 80 GB | 48 GB |
| Memory type | HBM2e | GDDR6 |
| Bandwidth | 2039 GB/s(+136%) | 864 GB/s |
| Architecture | Ampere | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Datacenter | Datacenter |
| Released | 2020 | 2023 |
| Models (native) | 54 | 53 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA A100 80GB | NVIDIA L40S | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 38.8 t/s(Q6_K) | 24.7 t/s(Q4_K_M) | +57% |
| Qwen 3.6 27B(27B) | 37.8 t/s(FP16) | 32 t/s(Q8) | +18% |
| Llama 3.1 8B Instruct(8B) | 127.4 t/s(FP16) | 54 t/s(FP16) | +136% |
| Qwen 2.5 7B Instruct(7.6B) | 134.1 t/s(FP16) | 56.8 t/s(FP16) | +136% |
Delta is NVIDIA A100 80GB relative to NVIDIA L40S.
Only NVIDIA A100 80GB can run(1)
Only NVIDIA L40S can run(0)
No exclusive models — NVIDIA A100 80GB can run everything NVIDIA L40S can.
Both run natively(53)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 122B-A10B (MoE)448.6 t/svs316.8 t/s
- Nemotron 3 Super 120B373.8 t/svs264 t/s
- GPT-OSS 120B897.2 t/svs633.6 t/s
- Llama 4 Scout 109B263.9 t/svs186.4 t/s
- GLM-4.5 Air 106B299.1 t/svs264 t/s
- GLM-4.6V 106B299.1 t/svs264 t/s
- Qwen 2.5 72B Instruct37.8 t/svs24 t/s
- Llama 3.3 70B Instruct38.8 t/svs24.7 t/s
- DeepSeek R1 Distill Llama 70B38.8 t/svs24.7 t/s
- Llama 3.1 70B Instruct38.8 t/svs24.7 t/s
- Mixtral 8x7B Instruct v0.1173.9 t/svs98.2 t/s
- Command-R 35B58.3 t/svs32.9 t/s
- Qwen 3.5 35B-A3B (MoE)747.6 t/svs316.8 t/s
- Qwen 3.6 35B58.3 t/svs24.7 t/s
- Yi 1.5 34B Chat59.3 t/svs25.1 t/s
- Qwen3 32B31.1 t/svs26.3 t/s
- +37 more on both
Which should you choose?
Choose NVIDIA A100 80GB if:
- • You need to run larger models (>48 GB VRAM)
- • Faster token generation is the priority
Choose NVIDIA L40S if:
- • You want the newer architecture and longer driver support lifecycle
Frequently asked questions
- Which is better for local AI, the NVIDIA A100 80GB or NVIDIA L40S?
- For local AI inference, the NVIDIA A100 80GB has the edge. It offers 80 GB VRAM (vs 48 GB) and 2039 GB/s bandwidth (vs 864 GB/s), letting it run 54 models natively in VRAM vs 53 for its rival.
- How much VRAM does the NVIDIA A100 80GB have vs the NVIDIA L40S?
- The NVIDIA A100 80GB has 80 GB of HBM2e at 2039 GB/s. The NVIDIA L40S has 48 GB of GDDR6 at 864 GB/s. The NVIDIA A100 80GB has 32 GB more VRAM, allowing it to run 1 models the NVIDIA L40S cannot fit natively.
- Can the NVIDIA A100 80GB run Llama 3.3 70B?
- Yes. The NVIDIA A100 80GB runs Llama 3.3 70B natively at Q6_K quantization at approximately 38.8 tokens per second.
- Can the NVIDIA L40S run Llama 3.3 70B?
- Yes. The NVIDIA L40S runs Llama 3.3 70B natively at Q4_K_M quantization at approximately 24.7 tokens per second.
- What is the difference between the NVIDIA A100 80GB and NVIDIA L40S for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA A100 80GB has 80 GB VRAM at 2039 GB/s (CUDA backend). The NVIDIA L40S has 48 GB VRAM at 864 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA A100 80GB runs 54 models natively vs 53 for the NVIDIA L40S.