Specialized LLM Inference Hosts: Groq LPUs, Together, Fireworks vs Hyperscalers – 2026 Throughput and Cost Guide

Overview of Specialized Inference Hosts Specialized LLM inference hosts like Groq, Together AI, and Fireworks AI are optimized platforms designed for high-throughput, low-latency production workloads. Unlike general-purpose hyperscalers (AWS Bedrock, Azure AI, Google Vertex AI), these providers focus on inference efficiency using custom hardware or software stacks. - Groq : Leverages Language Processing Units (LPUs), a deterministic, software-first architecture with on-chip memory for ultra-low latency. Ideal for real-time agentic workflows in platforms like LUMOS multi-agent RAG systems. (Source: ) - Together AI : Employs distributed GPU inference with a vast open model catalog, supporting fine-tuning and serverless scaling. - Fireworks AI : Specializes in GPU-optimized inference, emphasizing speed and a broad selection of quantized models. These hosts excel in tokens/sec and tokens/$ f

or open-weight models (e.g., Llama, Mistral), making them attractive for B2B leaders evaluating AI operations in 2026. However, they require planning around catalog gaps and cold starts. All data referenced as-of May 5, 2026 (UTC); always verify official docs. Throughput Benchmarks: Tokens/Sec vs Hyperscalers Throughput (tokens per second, TPS) measures inference speed under load, critical for high-volume RAG or agent workflows. Specialized hosts shine here due to hardware optimizations. Groq LPUs lead public benchmarks: - llama3-groq-70b-8192 : Up to 500-1,000+ TPS in production (per Groq console benchmarks, ). - mixtral-8x7b-32768 : 400-800 TPS. Together AI reports 200-500 TPS for Llama 3.1 models on A100/H100 clusters ( ). Fireworks claims similar on 'llama-v3p1-70b': 300+ TPS with optimizations ( ). Hyperscalers lag: - AWS Bedrock (Llama 3.1 70B): 50-150 TPS on GPU instances. - Azure

OpenAI: Comparable, with provisioning needed for peaks. Methodology : TPS varies by batch size, quantization (e.g., FP8), and concurrency. Use provider dashboards or (secondary) for model id-specific tests. For LUMOS agents, prioritize 300 TPS to minimize fleet costs. Cost Analysis: Tokens/$ from Official Pricing Effective cost is tokens/$ = (input $/M input ratio + output $/M output ratio) / TPS. Always pull from primary sources; prices fluctuate with tiers. As-of May 5, 2026: Provider Model ID Example Input $/1M Tokens Output $/1M Tokens Source ---------- ------------------ ------------------- -------------------- -------- Groq llama-3.1-70b-versatile $0.59 $0.79 Groq mixtral-8x7b-32768 $0.27 $0.79 Together AI meta-llama/Llama-3.1-70B-Instruct $0.20 $0.20 Fireworks accounts/fireworks/models/llama-v3p1-70b $0.20 $0.20 AWS Bedrock meta.llama3.1-70b-instruct-v1:0 $0.82 (per 1M, on-demand

) $4.15 Notes: Hyperscaler prices include markup; specialized hosts offer batch discounts (e.g., Together 50% off). Compute tokens/$ assuming 4:1 input:output and 300 TPS: Groq $0.005/token vs Bedrock $0.015. Verify tiers (e.g., Groq Free Tier limits). No guarantees—test via APIs. Cold Start Latency Realities Cold starts occur in serverless inference when scaling from zero, impacting agentic workflows (e.g., sporadic LUMOS queries). - Groq : <100ms p50 latency, near-instant due to LPU design ( ). Minimal cold starts. - Together/Fireworks : 500ms-2s typical; use persistent endpoints to avoid ( , ). - Hyperscalers : 5-30s+ on Bedrock/Azure serverless; provisioned throughput units (PTUs) mitigate but add fixed costs. Planning tip : For RAG/agents, route low-volume traffic to warmed pools or hybrids. Quantitative data from vendor perf docs or (secondary). Model Catalog Gaps and Availability

Specialized hosts prioritize open models but lack closed frontiers. Groq Catalog ( ): - Strengths: llama-3.2-90b-preview, gemma2-27b, distil-whisper. - Gaps: No Claude, Gemini, o1-style reasoning natives; limited multimodal. Together AI ( ): - 200+ models: qwen2.5-72b, deepseek-r1; fine-tunes. - Gaps: Fewer proprietary reasoning (e.g., no Sonnet 3.7). Fireworks ( ): - Broad: llama-3.3-70b, mistral-large; quantization options. - Gaps: Emerging MoE/reasoning models lag hyperscalers. Enterprise impact : For LUMOS RAG/agents, map workflows—use Groq for latency-critical (Llama), Together for variety. Gaps in reasoning (e.g., missing 'o1-mini' equiv) mean multi-provider routing. Hyperscaler Baselines: AWS Bedrock, Azure, GCP Hyperscalers offer reliability and compliance but trade speed/cost. - AWS Bedrock : Vast catalog (Claude, Llama, Titan); pricing per above. Throughput: Scale via PTUs ($fi

xed/hour). ( ) - Azure AI : OpenAI models + open ('azure-openai/gpt-4o'); +10-20% markup vs direct. Cold starts provisionable. ( ) - GCP Vertex AI : Gemini + Llama; batch discounts. TPS lower without custom tuning. vs Specialized : Hyperscalers win on SLAs/security; hosts on perf/$. Hybrid for 2026.