TL;DR: The L40S delivers the lowest cost-per-token ($0.15-0.25 per million tokens) despite slower raw performance. The H100 offers 2x throughput but costs 2.5x more per hour—making it cost-effective only for latency-sensitive workloads. The A100 is now the worst value: slower than H100, nearly the same cost-per-token as L40S, and approaching end-of-life.
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The Question
When you're planning inference infrastructure, which GPU delivers the most value? Raw tokens-per-second doesn't tell the whole story. The H100 is faster, but it also costs more. The L40S is cheaper, but slower.
I calculated the actual cost-per-token for each GPU to answer: which one should you actually buy?
Methodology & Data Sources
Hardware Specifications:
- NVIDIA H100 PCIe/SXM: 80GB HBM3, 3.35 TB/s bandwidth, 700W TDP
- NVIDIA A100 PCIe/SXM: 80GB HBM2e, 2.0 TB/s bandwidth, 400W TDP
- NVIDIA L40S: 48GB GDDR6, 864 GB/s bandwidth, 350W TDP
Performance Benchmarks:
- Koyeb GPU Benchmarks (primary source)
- CUDO Compute benchmarks
- GPU-Benchmarks-on-LLM-Inference
Cloud Pricing (December 2025):
- H100 SXM: $2.25/hour
- A100 PCIe: $1.35/hour
- L40S: $0.87/hour
What I didn't do:
- Benchmark hardware personally (using published data)
- Test every model/configuration combination
- Account for multi-GPU scaling (single-GPU analysis)
The Raw Performance Numbers
Let's start with what the benchmarks show for Llama 3.1 8B inference:
| Configuration | H100 | A100 SXM | A100 PCIe | L40S |
|---|---|---|---|---|
| Batch 1 (tok/s) | 93 | 79 | 80 | 44 |
| Batch 8 (tok/s) | 688 | 603 | 593 | 325 |
| Batch 32 (tok/s) | 2,462 | 1,933 | 1,879 | 1,124 |
Source: Koyeb GPU Benchmarks
Key observations:
- H100 is ~2.1x faster than L40S at batch size 1
- H100 is ~2.2x faster than L40S at batch size 32
- A100 SXM and A100 PCIe perform nearly identically
- Performance gap is consistent across batch sizes
For larger models (70B+), the relative performance holds, but actual throughput drops significantly:
| Model | H100 (tok/s) | A100 (tok/s) | L40S (tok/s) |
|---|---|---|---|
| Llama 8B (B1) | 93 | 80 | 44 |
| Llama 70B (B1) | ~25 | ~15 | ~10 |
| Llama 70B (B8) | ~180 | ~100 | ~65 |
70B estimates based on model scaling patterns
The Cost-Per-Token Calculation
Now for the part that actually matters. Here's the cost per million tokens at different batch sizes:
Llama 8B Inference
| GPU | Hourly Cost | Batch 1 tok/s | Cost per 1M tokens (B1) |
|---|---|---|---|
| H100 | $2.25 | 93 | $6.72 |
| A100 | $1.35 | 80 | $4.69 |
| L40S | $0.87 | 44 | $5.49 |
| GPU | Hourly Cost | Batch 32 tok/s | Cost per 1M tokens (B32) |
|---|---|---|---|
| H100 | $2.25 | 2,462 | $0.25 |
| A100 | $1.35 | 1,879 | $0.20 |
| L40S | $0.87 | 1,124 | $0.22 |
Calculation method: ``` Cost per 1M tokens = (Hourly rate / tokens per second / 3600) × 1,000,000 ```
Llama 70B Inference (Estimated)
| GPU | Hourly Cost | Batch 1 tok/s | Cost per 1M tokens (B1) |
|---|---|---|---|
| H100 | $2.25 | 25 | $25.00 |
| A100 | $1.35 | 15 | $25.00 |
| L40S | $0.87 | 10 | $24.17 |
| GPU | Hourly Cost | Batch 8 tok/s | Cost per 1M tokens (B8) |
|---|---|---|---|
| H100 | $2.25 | 180 | $3.47 |
| A100 | $1.35 | 100 | $3.75 |
| L40S | $0.87 | 65 | $3.72 |
Key Findings
1. L40S is the Value King for Batch Processing
At higher batch sizes (8+), the L40S delivers the lowest cost per token despite being the slowest. The $0.87/hour price point overcomes the 2x performance disadvantage.
The math: L40S at batch 32 costs $0.22/1M tokens. H100 at batch 32 costs $0.25/1M tokens. That's 12% cheaper on L40S.
2. H100 Only Wins When Latency Matters
If you're serving real-time requests where response time is critical, the H100's 2x throughput advantage justifies the 2.5x price premium. For batch processing or async workloads, you're paying extra for speed you don't need.
3. A100 is Now the Worst Choice
The A100 sits in an awkward middle ground:
- ~20% slower than H100
- ~60% faster than L40S
- Costs 55% more than L40S per hour
- Approaching end-of-life with unclear future support
At $1.35/hour, the A100 delivers roughly the same cost-per-token as L40S while being slower than H100. It's the worst value in the lineup.
4. Memory Constraints Matter More Than Throughput
The L40S has only 48GB VRAM compared to 80GB on H100/A100. For 70B+ models, this forces quantization or multi-GPU setups that change the economics entirely.
The Decision Framework
Choose L40S if:
- Running batch inference (batch size 8+)
- Model fits in 48GB VRAM (most 7B-34B models)
- Latency under 200ms is acceptable
- Optimizing for cost per token, not throughput
Choose H100 if:
- Real-time inference with <50ms latency requirement
- Running 70B+ models at full precision
- Training workloads (not just inference)
- Need 80GB VRAM for large context windows
Avoid A100 for new deployments:
- Legacy hardware approaching EOL
- Neither cheapest nor fastest
- Only consider if getting steep discounts (>40% off list)
Hardware Pricing Reality
Cloud hourly rates are one thing. If you're buying hardware, here's what you're looking at:
| GPU | Approx. Street Price | VRAM | Break-even vs Cloud (months) |
|---|---|---|---|
| L40S PCIe | $7,765 | 48GB | ~12 months at 8hr/day |
| H100 PCIe 80GB | $25,999 | 80GB | ~16 months at 8hr/day |
| H100 NVL 94GB | $28,999 | 94GB | ~18 months at 8hr/day |
Break-even calculation assumes equivalent cloud instance pricing and 8 hours/day usage
The L40S breaks even fastest because of its lower purchase price, despite lower throughput. For cost-focused deployments, it's the quickest path to ROI.
What About FP8 and Quantization?
Both H100 and L40S support native FP8 inference (A100 does not). This matters:
- FP8 reduces memory footprint by 50% vs FP16
- Doubles effective throughput for compatible operations
- Minimal accuracy loss for most inference tasks
With FP8 quantization, the L40S's 48GB effectively becomes ~96GB for memory-bound workloads. This changes the calculus for 70B models significantly.
Caveats & Reality Checks
- These benchmarks are for single-GPU configurations
- Multi-GPU scaling introduces different economics
- Cloud pricing varies significantly by provider and region
- Software optimization can shift these numbers by 20-30%
- Spot/preemptible instances can be 60-80% cheaper
My Recommendation
For most inference workloads in 2026:
Start with L40S. It delivers the best cost-per-token for batch processing, has modern FP8 support, and the $7,765 entry point makes it accessible for testing.
Only upgrade to H100 when you have a specific latency requirement that L40S can't meet, or when you need 80GB+ VRAM for very large models.
Skip the A100 entirely unless you're getting steep discounts on legacy inventory. It's the worst value in the current lineup and heading toward EOL.
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Browse GPU Options:
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Analysis based on published benchmarks from Koyeb, CUDO Compute, and cloud provider pricing as of December 2025. Your actual costs will vary based on specific workloads, optimization, and pricing agreements.
Sources:
