Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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.
Show the rest of the publisher’s description (6 more lines)
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.
Highlights
Highlighted by the publisher on AWS Marketplace.
30-70% throughput improvement over default configurations with zero manual tuning.
Fully autonomous closed-loop optimization - deploys, benchmarks, analyzes, and tunes with automatic rollback on regression.
Single GPU container deploys in minutes - no separate backend infrastructure required.
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment
Plans and pricing as listed
1 listed- Users
Refund terms
As stated by the publisher on AWS Marketplace.
SwarmOne offers a full refund within the first 3 days of your initial subscription if the product does not meet your expectations. After the 3-day period, subscriptions are non-refundable and will remain active until the end of the current billing cycle. Cancellations take effect at the end of the billing period. To request a refund or cancel your subscription, contact benb@swarmone.ai.
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.