01
Deploy at the edge
We are industrializing the Blackwell architecture for telecom environments. Stations near users—at carrier POPs or tower-adjacent sites—so inference happens locally.
Edge inference for voice, vision, and LLMs
Run AI where your users are—at telecom scale.
SwiftInference deploys multi-tenant edge stations at carrier and tower sites to cut latency, reduce bandwidth, and deliver predictable inference economics.
Guaranteed slots + spot capacity
Streaming-first (voice & video)
Fleet attestation & secure updates
SwiftFabric
Edge → Cloud control plane
Route requests to the best node, enforce SLAs, and measure p50/p95/p99 in real time.
Median latency
19.4 ms
Local metro edge
Bandwidth saved
45%
By shifting inference closer
Uptime target
99.9%
With auto-drain + reroute
Per-node capacity model
Slot A
Anchor
Guaranteed performance
Slot B
Secondary
Guaranteed performance
Slot C
Spot
Interruptible / best-effort
We sell guarantees for A/B. Slot C is optional, priced like spot compute.
How SwiftInference works
Drop-in edge inference that behaves like cloud: auth, routing, quotas, SLAs, and observability.
02
Pin models & enforce slots
Two reserved slots provide predictable performance. A third optional spot slot absorbs bursty workloads.
03
Route intelligently
SwiftFabric routes each request to the best node based on health, load, latency, and policy—then streams results.
04
Operate safely
Secure boot, attestation, signed updates, and per-tenant isolation guard workloads and infrastructure.
Designed for real-time
Voice and video workloads punish jitter. SwiftInference is streaming-first with admission control to protect tail latency.
The platform
Everything you need to run multi-tenant inference at edge sites—without turning into a hardware company.
SwiftFabric (Control Plane)
Provision nodes, place tenants, enforce quotas, and monitor p50/p95/p99 across the fleet.
- Policy routing and failover
- Per-tenant metering & billing events
- OTA updates with staged rollouts
SwiftEdgeOS (Runtime)
Hardened host + container runtime + inference stack tuned for streaming workloads.
- Admission control & priority scheduling
- Model pinning & warm refresh
- Secure telemetry & tracing
SwiftSlots (Commercial Model)
Sell predictable performance where it matters, and monetize excess capacity without breaking SLAs.
- Two guaranteed tenant slots
- Optional interruptible spot slot
- Clear isolation + eviction rules
Request path
- Ingress User traffic enters via fiber / 5G.
- Route SwiftFabric selects the best edge station.
- Infer Models remain pinned in memory for low jitter.
- Stream Tokens/audio/video stream back immediately.
- Observe Tail latency is protected by admission control.
Use cases
Three “slots” per node map cleanly to real workloads.
🗣️
Real-time voice AI
Low-latency STT/TTS and conversational agents near users. Protects jitter and improves turn-taking.
Slot A/B
Streaming
👁️
Computer vision at the edge
Reduce backhaul by processing video locally: object detection, safety, retail analytics, and industrial monitoring.
Slot B/C
Bandwidth saver
🧠
LLM inference & RAG
Serve token streams closer to users. Run embeddings on spot capacity and keep the main model pinned.
Slot A
Pinned weights
Built to integrate with
vLLM
Triton
TensorRT-LLM
Kubernetes*
Envoy
*Kubernetes optional; SwiftEdgeOS can run lightweight for constrained sites.
Pricing
Spot
Best-effort capacity
$999/mo
- On demand workloads
- Contended
Most popular
Guaranteed
Secondary slot
$2,999/mo
- Guaranteed throughput
- Priority scheduling
- Latency SLO reporting
Anchor
Primary slot + enterprise
$3,999/mo
- Highest priority
- Custom routing + SLAs
- Dedicated support
Pricing shown is illustrative for early deployments. Final pricing depends on site density, workload characteristics, and SLA terms.
FAQ
Answers to the questions buyers frequently ask.
How many customers can share a single node?
Two customers get guaranteed slots. A third optional spot slot is interruptible. We don’t oversell beyond that because tail latency matters.
Is SwiftInference a hardware company?
No. We focus on the platform: routing, slot enforcement, observability, and secure operations. We deploy on best-fit edge systems for the site class.
How do you protect customer IP and models?
Secure boot, signed updates, and node attestation are required before workloads run. Models can be encrypted at rest with keys released only to trusted nodes.
What’s the fastest way to pilot?
Pick one metro area and 20–50 edge sites (carrier POPs or tower-adjacent). We deploy, integrate your runtime, and tune for your p95/p99 targets.
Request a pilot
Tell us your workload (voice, vision, LLM), target metros, and SLA requirements. We’ll respond with a deployment plan, slot sizing, and pricing.
Email
hello@swiftinference.ai
Partnerships
partners@swiftinference.ai
We can provide NDA-ready technical docs and pilot KPIs on request.