NVIDIA A100 80GB vs NVIDIA L40S
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 110 open-weight models.
Quick verdict
NVIDIA A100 80GB wins for local AI inference. It has 32 GB more VRAM and 136.0% more memory bandwidth, runs 74 models natively (vs 64), and exclusively fits 10 models the other cannot.
Analysis
The NVIDIA A100 80GB and L40S sit in different corners of NVIDIA's datacenter lineup: the A100 is a 2020 HBM-based compute accelerator, the L40S a 2023 Ada Lovelace card built as much for graphics and video workloads as for AI. For local LLM inference, the practical question is whether the L40S's newer architecture and lower power draw make up for giving up 32GB of capacity and well over half the memory bandwidth.
The A100 leads on both axes that matter for LLM inference: 80GB versus the L40S's 48GB (+67%), and 2,039 GB/s versus 864 GB/s (+136%) thanks to HBM2e over GDDR6. That gap compounds on real models: this site's calculator fits Llama 3.3 70B at Q6_K (67.37 GB, 16.3 tok/s) on the A100, three rungs above the L40S's best fit of Q3_K_M (40.72 GB, 15.0 tok/s); even at that same Q3_K_M quantization, the A100 decodes 71% faster (25.6 tok/s) than the L40S manages at its own ceiling, well short of the two cards' 2.36x bandwidth gap because single-stream llama.cpp does not saturate HBM. Across the 110 models this site tracks, the A100 runs 74 natively versus the L40S's 64. The L40S's real advantages sit elsewhere: Ada Lovelace's FP8 Transformer Engine, which Ampere lacks; hardware video encode/decode for mixed multimedia pipelines; and a 300W power draw against the A100 SXM4's 400W.
Bottom line: For LLM inference specifically, the A100 80GB is the stronger card on every metric that determines what fits and how fast it runs; pick it when local models are the primary workload and 80GB of HBM headroom matters. The L40S makes more sense when the deployment is genuinely mixed-use: video transcoding or rendering alongside inference, a tighter power or thermal budget, or a workload where Ada Lovelace's newer FP8 path matters more than raw capacity. As a pure LLM box, the extra 32GB and 2.4x bandwidth on the A100 are hard to give up.
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) | 74 | 64 |
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) | 16.3 t/s(Q6_K) | 15 t/s(Q3_K_M) | +71%at Q3_K_M |
| Qwen 3.6 27B(27B) | 18.1 t/s(BF16) | 18.7 t/s(Q8_0) | +71%at Q8_0 |
| Llama 3.1 8B Instruct(8B) | 55.5 t/s(BF16) | 32.3 t/s(BF16) | +72% |
| Qwen 2.5 7B Instruct(7.6B) | 60.8 t/s(BF16) | 35.3 t/s(BF16) | +72% |
Delta is NVIDIA A100 80GB relative to NVIDIA L40S, compared at the highest quantization that fits natively on both cards.
Only NVIDIA A100 80GB can run(10)
Only NVIDIA L40S can run(0)
No exclusive models: NVIDIA A100 80GB can run everything NVIDIA L40S can.
Both run natively(64)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct15.9 t/svs14.7 t/s
- Llama 3.3 70B Instruct16.3 t/svs15 t/s
- DeepSeek R1 Distill Llama 70B16.3 t/svs15 t/s
- Llama 3.1 70B Instruct16.3 t/svs15 t/s
- Mixtral 8x7B Instruct v0.160.4 t/svs44.5 t/s
- Flint 40B (MoE)147.6 t/svs125.5 t/s
- Command-R 35B21 t/svs14.4 t/s
- Qwen 3.5 35B-A3B (MoE)155.2 t/svs111.3 t/s
- Qwen 3.6 35B-A3B (MoE)155.2 t/svs111.3 t/s
- Ornith 1.5 35B-A3B (MoE)155.2 t/svs111.3 t/s
- Yi 1.5 34B Chat25 t/svs14.5 t/s
- Nanbeige4.3 33B26.4 t/svs15.2 t/s
- Qwen3 32B14.9 t/svs15.3 t/s
- Qwen 2.5 32B Instruct14.9 t/svs15.1 t/s
- Qwen 2.5 Coder 32B Instruct14.9 t/svs15.1 t/s
- DeepSeek R1 Distill Qwen 32B14.9 t/svs15.1 t/s
- +48 more on both
Which should you choose?
- • You need to run larger models (>48 GB VRAM)
- • Faster token generation is the priority
- • 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 74 models natively in VRAM vs 64 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 10 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 16.3 tokens per second.
- Can the NVIDIA L40S run Llama 3.3 70B?
- Yes. The NVIDIA L40S runs Llama 3.3 70B natively at Q3_K_M quantization at approximately 15 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 74 models natively vs 64 for the NVIDIA L40S.