Inference scaling is a variance problem, not a volume problem. Steady 10 req/s is trivial. A 2 req/s baseline with bursts to 200 req/s forces tradeoffs between replica scheduling, cold-start latency, and GPU utilization. This breaks down how Yobibyte handles that, based on their product docs and knowledge base. Not affiliated with Yobitel; vendor claims are flagged as claims.
Serverless: scale-to-zero
Replicas scale to zero at idle, no standing GPU cost. Cold start on a new request: under 30 seconds (vendor-stated, unverified here). Fine for async workloads. A hard SLA violation for synchronous endpoints with sub-second p99 targets. Billing is per-request, not per-instance-hour.
GPU tiers: matching hardware to model size
| GPU | Memory | Fit | Price from |
|---|---|---|---|
| T4 | 16 GB | Small models | $0.50/hr |
| A10G | 24 GB | Entry inference | $0.80/hr |
| L4 | 24 GB | Balanced inference | $0.90/hr |
| A100 | 40/80 GB | Training + inference | $2.10/hr |
| H100 | 80 GB | Production inference | $3.50/hr |
| MI300X | 192 GB | Memory-bound LLM serving | $4.20/hr |
| H200 | 141 GB | Large model inference | $4.80/hr |
| B200 | 192 GB | Multi-node training | $6.20/hr |
| B300 | 288 GB | Frontier training | $9.00/hr |
7B models fit on T4/A10G. 70B models need H100/H200 minimum. Undersizing causes OOM or throughput loss. Cross-vendor (NVIDIA + AMD) support avoids single-roadmap lock-in.
On-demand vs reserved
On-demand: dedicated GPU, root access, no cold start, cost regardless of utilization. For latency-critical endpoints that can't scale to zero.
Reserved: 1–3 year commitments, InfiniBand-connected multi-node clusters, up to 32% savings claimed over on-demand. For sustained, predictable production load and tensor-parallel/distributed inference.
Training at scale
LoRA, QLoRA, full fine-tune, RLHF/DPO on H100/H200/B200 clusters, multi-node distributed. Automatic checkpointing protects against node failure mid-run, failure probability rises with node count, so checkpointing is not optional at scale. Fine-tuned models deploy through the same interface as base models, no separate export step.
Observability
GPU-level metrics, distributed tracing, anomaly detection with automated remediation. Yobitel claims up to 90% MTTR reduction, unverified independently, treat as a hypothesis to test against your own incident data.
Verified vs unverified
Documented and consistent: deployment modes, GPU specs and pricing, fine-tuning methods, checkpointing behavior.
Vendor claims, not independently tested: 30-second cold start, 32% reserved savings, 90% MTTR reduction.
FAQ
How does Yobibyte scale from zero traffic?
Serverless mode scales replicas to zero at idle, provisions on demand, claimed under 30-second cold start.
When should you avoid serverless?
When p99 latency SLA is sub-second, or traffic is steady enough that on-demand's flat cost beats per-request billing.
What GPU for a 70B model?
H100 (80GB) or H200 (141GB) minimum.
What prevents distributed training loss on node failure?
Automatic checkpointing across multi-node H100/H200/B200 clusters.
Is the 90% MTTR claim verified?
No, it's Yobitel's own stated figure.
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