Swarm Optimizer

SwarmOne Optimizer is a GPU-native container that autonomously tunes your LLM inference server for peak performance. Point it at a model, and it handles everything: deploying the inference server, running production-representative benchmarks, analyzing results with AI, generating improved configurations, and repeating until convergence.

The Problem: Running large language models in production is expensive. A misconfigured parameter - batch size, KV cache allocation, tensor parallelism degree, quantization setting - can cut throughput in half or double tail latency. Teams spend days hand-tuning through trial and error, only to discover their settings are suboptimal for their actual traffic patterns.

How It Works: The optimizer runs a closed-loop optimization cycle directly on your GPU infrastructure. It begins by deploying your model with current settings and running benchmarks that capture time-to-first-token (TTFT), inter-token latency (ITL), throughput, and GPU utilization. A specialized optimization agent then analyzes your hardware topology, model architecture, and current metrics to identify bottlenecks. It produces a new configuration with specific parameter changes and technical rationale for each. The new configuration is deployed, benchmarked, and compared against the baseline, with automatic rollback on regression. This cycle repeats until convergence, typically within 3 to 8 iterations.

What You Get: 30 to 70 percent throughput improvement over default configurations in typical deployments. 2 to 5x reduction in P99 latency through intelligent batching and memory allocation tuning. Full hardware awareness including NVLink vs PCIe topologies, mixed GPU generations, and memory hierarchies. Framework-native tuning with deep knowledge of vLLM internals including chunked prefill, speculative decoding, prefix caching, and PagedAttention parameters. Production-safe operation where every configuration change is benchmarked before promotion. Complete audit trail of every configuration attempted with before-and-after metrics. Continuous mode that keeps running after convergence, re-benchmarking periodically to detect drift.

Architecture: The product runs as a single container alongside the inference server it manages. It communicates with the SwarmOne cloud service for license validation and AI-powered configuration analysis. Your prompts, model weights, and inference data never leave your infrastructure.

Supported Configurations: vLLM framework on NVIDIA A100, H100, H200, L40S, A10G, and other CUDA-capable GPUs. Compatible with any HuggingFace model including Llama, Mistral, Mixtral, Qwen, DeepSeek, Gemma, and Phi. Supports single-node multi-GPU deployments.

Getting Started: Subscribe on AWS Marketplace, launch a GPU instance (p4d, p5, g5, g6, or g6e), set your license key and model name as environment variables, and the optimizer begins automatically. Most users see first optimization results within 15 minutes.

You're paying for the software here - hosting and infrastructure costs from your cloud provider are separate.