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Sylvaxo S
Sylvaxo
Software Studio
Aetheria GPU Orchestrator
AI AutomationClient: Aetheria ComputeShipped: 2024

Dynamic GPU Quantization & Multi-Hop Inference Network

Built an intelligent scheduling engine that routes machine learning inference jobs across heterogenous global GPU clusters, cutting cost-per-token by 52%.

DAILY TOKENS
418.9M
● 100% Cluster Availability
COST ARBITRAGE
-52.4%
vs AWS On-Demand
GLOBAL TTFT
184ms
Under 220ms SLA
THROUGHPUT
84.2 tok/s
vLLM Speculative Engine
Global Heterogeneous GPU Cluster Matrix (64 Clustered Accelerators)Mesh: Active Auto-Routing
US-EASTPRIMARY ROUTE
8x NVIDIA H100 SXM5 80GB
VRAM:72.4 / 80 GB (90%)
Temp:62°C
Speed:512 tok/s
US-WESTPRIMARY ROUTE
8x NVIDIA H100 SXM5 80GB
VRAM:58.1 / 80 GB (72%)
Temp:58°C
Speed:490 tok/s
EU-CENTRALHOT STANDBY
8x NVIDIA A100 TensorCore 80GB
VRAM:64.0 / 80 GB (80%)
Temp:68°C
Speed:310 tok/s
AP-TOKYOHOT STANDBY
8x NVIDIA L40S 48GB
VRAM:31.8 / 48 GB (66%)
Temp:54°C
Speed:240 tok/s
Dynamic Quantization & Inference Benchmark TesterEngine: vLLM PagedAttention
Streaming Response StreamTTFT: 184ms
Architecture Blueprint:
1. Edge-first Ingestion: Deploy localized Go micro-workers listening on binary TCP sockets.
2. Ring Buffer Cache: Warm Redis ring buffers locally to absorb highway dropouts.
3. Kafka Partitions: Route vehicle telemetry deterministically across 32 partition brokers.
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