What changes
- Replace ongoing GPU rental with owned capacity.
- Reduce egress and transfer costs by serving locally.
- Increase utilization via multi-model / multi-tenant scheduling.
White paper
SwiftInference for AI Companies — Scaling High-Performance Inference On‑Premises
Own your inference: reduce cloud GPU OPEX, deploy distributed POPs, and deliver fast streaming experiences with tight p99.
SwiftFabric
Why this exists
Cloud inference is often too far away; on-device is too small. SwiftInference gives you cloud-grade models at edge latency.
Placement
Near users
Towers / carrier POPs
Commercial model
Slots
Guaranteed + spot
Operating model
Fleet
Attestation + OTA
Product overview for AI companies
SwiftInference is a distributed inference platform you can deploy at edge colos, customer sites, or telco POPs — to reduce latency, cut inference OPEX, and keep control of data and IP.
Goal
Own inference
Stop renting performance forever.
Deployment
Distributed
Metro POPs, colos, on‑prem.
Runtime
Streaming-first
Fast TTFT, stable p99.
ROI & economics
If you have steady inference load, cloud rent becomes a tax. SwiftInference shifts you to capex + power, then amortizes across your usage.
Payback intuition
- If you spend roughly $1k/month on inference, a ~$4k node can pay back in a handful of months.
- Full utilization drives cost-per-query down dramatically.
- On‑prem compliance unlocks enterprise deals cloud can’t win.
Margin lever
- Offer “edge premium” tiers: lower latency, data residency, dedicated throughput.
- Use spot capacity for embeddings/batch and keep interactive on reserved.
Performance, stability, efficiency
Dedicated edge nodes avoid WAN jitter and noisy neighbors. Unified memory reduces copy overhead and helps keep latency distributions tight.
🚀
Throughput
Quantization-first serving (INT8/FP8/4-bit) to maximize tokens/sec and images/sec for production workloads.
vLLM/TritonTensorRT-LLM
🧰
Operational stability
Appliance-like runtime, containerized deployment, staged OTA rollouts — fewer “DIY GPU rig” surprises.
SRE-friendlyFleet ops
⚡
Performance per watt
Edge-class power envelopes make it feasible to deploy many POPs without data‑center‑scale power builds.
EfficientEdge deployable
Use cases
Deploy closer to users, or deploy inside customer networks. Either way, you win on latency + control.
01
OpenAI-style inference endpoints
Serve chat/completions with fast time-to-first-token and streaming output. Place nodes where your users are.
02
Voice AI
Run STT/TTS/translation in metro POPs for natural turn-taking and less jitter under burst.
03
Vision and video
Serve computer vision close to cameras for fast alerts and reduced upstream bandwidth.
04
V2X / autonomy support
Cooperative perception near corridors where latency deadlines are strict.
Competitive comparison
Cloud is elastic but expensive and far; on-device is fast but small. SwiftInference gives you a controllable middle layer.
SwiftInference
- Distributed POP strategy
- Lower tail latency
- Capex amortization
AWS / cloud GPU
- Opex-based; often high long-run cost
- Network variability, especially globally
- Provider dependency
On-device
- Great offline + privacy
- Model size constraints
- Device fragmentation
Pilot checklist
A practical rollout: start with 3–5 metros, then expand as usage grows. Treat it like building your own “inference CDN”.
Pick workloads
- Interactive: chat/voice (reserved)
- Batch: embeddings/RAG indexing (spot)
- Define SLOs for TTFT and p99
Integrate
- Drop-in compatible runtimes (vLLM/Triton/TensorRT)
- Use existing CI/CD to ship containers
- Enable tracing + metering
Decide packaging
- Edge premium tiers
- Dedicated nodes for regulated enterprises
- Hybrid: edge-first, cloud fallback
Security by default
Secure boot, node attestation, signed updates, and per-tenant isolation are built into SwiftEdgeOS.