Community Configurations

Minimal cost deployment for high-volume simple queries. INT4 quantization keeps it on a single affordable GPU.

Maximum throughput enterprise deployment of DeepSeek V3 MoE. H200 HBM3e memory eliminates bottlenecks for the 671B model.

Mixture-of-experts architecture activates only 37B of 671B params. Surprisingly affordable at scale.

High-throughput embedding generation for semantic search and vector databases. Tiny model, massive batch sizes.

Full-precision reasoning model for complex multi-step tasks. High throughput with tensor parallelism.

Consumer-grade deployment for personal or small team chat applications. RTX 4090 delivers surprising inference performance.

Maximize requests per second for Mistral 7B. H100 with continuous batching handles massive concurrent load.

Retrieval-augmented generation pipeline optimized for long-context document Q&A. FP8 keeps 70B on 2 GPUs with room for KV cache.

H200's extra HBM3e memory and bandwidth deliver 40% more throughput than H100 for large models.

Multimodal vision-language model for image understanding, OCR, and visual QA. Requires multi-GPU for the 90B variant.

Lightning-fast small model for simple tasks. FP8 on H100 maximizes throughput per dollar.

Optimized for IDE-integrated code completion and generation. Low latency on A100 with high batch throughput.

The largest open model for tasks where quality is paramount. Full BF16 for zero quality loss.

Mixtral MoE on A100 with AWQ quantization. Only 12.9B active params gives dense-like speed at fraction of the cost.

Optimized for code generation and review tasks. Great quality-to-cost ratio for developer tools.

Microsoft Phi-4 on NVIDIA L4 for cost-efficient edge inference. Great quality-to-size ratio for constrained deployments.

Tiny but capable model for edge and on-device inference. A10G keeps costs minimal.

Cost-effective setup for chat applications with strong quality. BF16 precision balances speed and accuracy.

Cohere Aya-23 for 23-language customer support. Excellent multilingual performance with efficient serving.

Original MoE value champion. Still competitive for many general-purpose tasks.

Good balance of cost and capability for enterprise use cases. Handles complex instructions well.

AWQ quantization for multilingual customer support. Excellent CJK language performance.