Commercial guide - Last reviewed 2026-06-10
L40S vs H100 ROI for LLM Inference
Compare L40S vs H100 ROI for LLM inference across throughput, memory, utilization, capex, and workload fit.
Direct answer for L40S vs H100 ROI
The short answer
L40S can have attractive ROI for smaller or latency-tolerant inference workloads. H100 tends to win when high throughput, larger models, memory bandwidth, and dense utilization justify the premium.
Choose L40S when model size, concurrency, and latency targets fit inside a lower-cost GPU profile.
Choose H100 when throughput, memory bandwidth, and serving density drive cost per million tokens down.
Compare cost per useful token, not raw benchmark speed.
Comparison table
| Factor | Option A | Option B |
|---|---|---|
| Workload fit | Smaller open models, embedding/rerank, lighter private inference. | Large models, heavy concurrency, strict throughput targets. |
| ROI driver | Lower capital cost and simpler capacity blocks. | Higher serving density and stronger cost per token at scale. |
| Risk | May need more cards or accept higher latency for large models. | Premium hardware hurts ROI if utilization is low. |
| Decision test | Can it meet p95 latency and quality without extra replicas? | Can it stay utilized enough to beat API or lower-tier GPU cost? |
Worked example
GPU fit by model class
- L40S: 48GB, ~$8-10K per card
- H100: 80GB HBM3, ~$30K+ per card
- Production concurrency, p95 latency floor
- Illustrative fits — your traffic shape decides
| Workload | L40S | H100 |
|---|---|---|
| 7-13B model serving | Strong fit: model + cache fit one card; best capex per token for this class | Works but typically overkill — premium pays off only at very high density |
| Embedding / rerank / TTS | Excellent ROI; these models rarely need HBM bandwidth | Wasted capability for most retrieval workloads |
| 70B-class serving | Needs 4+ cards with sharding; interconnect and latency suffer | 2 cards with INT8 + KV-cache pruning (benchmark: 2.3x throughput, ~$0.47/M tokens) |
| Capex for a 70B deployment | 4-6 cards ≈ $40-60K plus complexity | 2 cards ≈ $60K+ but simpler serving and better $/token at load |
Match the card to the model class: L40S wins ROI for 13B-and-under serving and retrieval workloads; for 70B-class production serving, two optimized H100s beat a larger L40S fleet on cost per token and operational simplicity.
Frequently asked questions
Is H100 always better than L40S for inference?
No. H100 is more capable, but ROI depends on utilization, model size, latency target, and whether throughput gains reduce total system cost.
What metric should decide L40S vs H100?
Use cost per accepted output at the required latency and quality level, including replicas, idle time, power, hosting, and operations.
Is the L40S good enough for Llama 70B?
Only with aggressive quantization and multi-card sharding, and the interconnect becomes the bottleneck. At 48GB per card, 70B-class serving needs several L40S cards where 2 optimized H100s suffice — our benchmark served Llama 3 70B at 2.3x baseline throughput on 2 H100s with INT8 and KV-cache pruning. For 70B at production concurrency, H100-class hardware usually wins.
What does H100 inference cost per million tokens?
Workload-dependent, but our published benchmark measured optimized Llama 3 70B at roughly $0.47 per million tokens on H100-class hardware including amortization — versus $0.82 unoptimized. Quantization strategy moves the number more than the GPU choice does.
References & related
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