Streams32
p999ms
Tokens/s140k
TOKRA LOOP

Loop


CPU ↔ GPU RUNTIME FABRIC
ROUTE · AUTO MODE · MATCH P99 · STABLE

WHY LOOP

More output. Same GPU.

Runtime fabric that accelerates your entire stack — deterministic p99 under load. Drop-in, no rewrites, vendor-agnostic, sovereign by design.

×2.33 Speedup vs Baseline
1,796 tok/s Tokra Runtime (Peak)
770 tok/s Baseline
~71.5 ms Mean Latency
LLM

Inference — reference

Deterministic p99. Overlap scheduling.

avg ×2.33 peak 1,796 tok/s
Social / Signals

Streaming analytics

Overlap + IO shaping on event streams.

avg ×4.8 up to ×6
Media

Live pipeline

LL-HLS. Jitter-safe tails.

1080p60 @ ~6 Mbps TTR 10.4s · 2–4s
no meetings • just build

This is where Loop ends — and your workload begins.

A deterministic runtime that keeps p99 flat under pressure. Above frameworks, beneath every workload. Vendor-agnostic, air-gapped ready.

What we guarantee

Deterministic p99
Stable tails under sustained load — fewer retries, predictable budgets.
No rewrites
Drop-in to existing stacks (Node / Python / Docker / K8s).
Sovereign by design
On-prem, air-gapped, keys stay inside.
LLM avg: ×2.33 peak: 1,796 tok/s p99: ~71.5 ms

Release track

Demo (live)
Playground & guided benchmark
now
Private trial
Invite-only, fully featured
open
General availability
Production SLA & support
soon

Ready on your stack

Works with your toolchain — no vendor lock-in.

NodePythonDocker KubernetesCPU↔GPUNVIDIA / AMD / Intel On-prem • Air-gapped
Full Benchmarks Early Access

Numbers from the reference build. For context & methods, see the full report above.