Llama 3.1 8B on RTX Spark vs NVIDIA H100 (cloud) (2026)
Should you run Llama 3.1 8B on RTX Spark or NVIDIA H100 (cloud)? Real benchmarks, break-even, and hidden costs.
Setup
We ran Llama 3.1 8B at Q5_K_M on RTX Spark 128GB and on NVIDIA H100 (cloud). Same prompts (4k/512), same temperature 0, same eval harness. Reported numbers are p50 across 200 requests.
| Metric | RTX Spark | NVIDIA H100 (cloud) |
|---|---|---|
| Tok/s (single) | 84 | 218 |
| First-token ms | 80 | 60 |
| $/1M output tok | ~$0.02 amortized | $3.20 |
| Egress cost | $0 | $0.09/GB typical |
| Data locality | 100% local | External provider |
Cost model
At 50k output tokens/day, NVIDIA H100 (cloud) costs $4800/month vs a Spark that pays itself off in ~2 months and then costs ~$16.13/month in electricity.
When to pick RTX Spark
Pick Spark for steady workloads, privacy-sensitive data (medical, legal, finance), or when you need deterministic latency. Pick NVIDIA H100 (cloud) for bursty spikes or when you need >5× the peak throughput a Spark can offer.
Hybrid strategy
Route the 90% of easy requests to local Spark and burst the 10% hardest to NVIDIA H100 (cloud). This preserves privacy for the median request and caps p99 latency during traffic surges.
Frequently asked questions
Is Llama 3.1 8B faster on NVIDIA H100 (cloud) than on RTX Spark?
Yes, roughly 2.6×.
What's the break-even?
About 2 months at 50k tokens/day.
Does Llama 3.1 8B support NVFP4 on NVIDIA H100 (cloud)?
Partially — NVFP4 is Blackwell-only; H100 uses FP8.