THE AGENT INFERENCE PROFILE: WHY STANDARD LLM SERVING FALLS SHORT
AI agents differ fundamentally from standard LLM inference. A single agent task may require 5-50 LLM calls in sequential or branching dependency chains: initial reasoning, tool selection, tool execution, result analysis, and output synthesis. Each call has its own prefill and decode phases, and intermediate results grow the context with each step. A single user query triggering an agentic workflow can consume 10-100x more GPU compute than a direct LLM query.
Standard LLM serving infrastructure optimized for stateless requests does not handle agentic workloads well. The mismatch is thread-level parallelism: agent tasks require per-task state across multiple inference steps. The emerging solution is an agent runtime layer that manages task state in CPU memory while dispatching individual inference steps to a stateless GPU inference pool.
FUNCTION-CALLING MODELS: THE AGENT BACKBONE
Multi-agent systems depend on models with strong function-calling capabilities. Llama-3.1-70B, Mistral-Large-2, and Qwen-2.5-72B all support structured tool calling with JSON schema. The inference pattern requires guiding decoding (outlines, grammars) that constrains output tokens to valid JSON, adding 5-15 percent overhead to decode time.
Function-calling models need tool schemas in the system prompt (2-10K tokens for complex setups). Prefix caching in vLLM/SGLang reduces the prefill cost for the system prompt prefix across requests. In production agent deployments, prefix caching reduces prefill time by 40-60 percent and should be considered mandatory infrastructure.
| Model | GPUs (TP) | Tool Acc (BFCL) | Max Tools | Guided Overhd | Use Case |
|---|---|---|---|---|---|
| Llama-3.1-8B | 1 H100 | 72% | 20 | 8% | Simple agents |
| Llama-3.1-70B | 4 H100 | 84% | 50 | 12% | Production default |
| Qwen-2.5-72B | 4 H100 | 86% | 80 | 10% | High accuracy |
| Mistral-Large-2 | 4 H100 | 83% | 60 | 15% | Agent chains |
| GPT-4o (API) | N/A | 88% | 100+ | N/A | Cloud-dependent |
MULTI-AGENT ORCHESTRATION PATTERNS ON GPU
Multi-agent systems (CrewAI, AutoGen, LangGraph) introduce DAG execution where agents run in parallel. GPU orchestration requires a scheduling layer that understands agent dependencies. The key optimization is opportunistic batching: when multiple agents wait on independent tool results, their next inference calls can be batched together, increasing GPU utilization from 20-30 percent to 55-70 percent.
Memory management is the primary constraint. Each agent maintains an independent context window; with 10 concurrent agents at 8K context, KV cache is 20-30 GB for a 70B model at FP16. At 50 agents, KV cache exceeds 100 GB, requiring larger GPU pools, aggressive context pruning, or KV cache offload to CPU memory.
| Agent Pattern | LLM Calls/Task | GPU Mem/Agent | Ideal Parallelism | Framework |
|---|---|---|---|---|
| Single + Tools | 3-8 | 2-4 GB (8K ctx) | 16 agents/GPU | LangChain |
| Sequential Chain | 5-15 | 3-5 GB (16K) | 8 agents/GPU | LangGraph |
| Hierarchical | 10-40 | 4-8 GB/agent | 4-6 agents/GPU | CrewAI |
| Group Chat | 20-100+ | 5-10 GB/agent | 2-4 GPUs/system | AutoGen |
| Router + Specialist | 15-60 | 3-6 GB avg | 8-12 agents/GPU | LangGraph |
TOOL EXECUTION: THE HIDDEN COMPUTE LAYER
Tool execution extends beyond LLM inference cost. Each tool call includes schema parsing (1-5 ms), tool execution (10 ms to 30 seconds), result serialization (1-3 ms), and context injection (<1 ms). For GPU-backed tools (image generation, audio transcription), execution runs on a dedicated tool-worker pool. CPU-backed tools (code interpreter, web search) run on CPU workers with configurable limits.
The tool execution layer typically costs 20-40 percent of total agent infrastructure. The sandbox security model is critical: Firecracker micro-VMs per agent session (125-150 ms boot, 256-512 MB RAM) are recommended for multi-tenant deployments. For 1,000 concurrent sessions, this requires approximately 50-60 CPU cores and 400-600 GB RAM.
AGENT-STATE CACHING AND PERSISTENCE
Managing agent state across inference steps is the defining infrastructure challenge. A single task can span minutes with state across 10-50 LLM calls: conversation history (20-200K tokens), tool output cache, agent persona, and execution DAG metadata. Redis or PostgreSQL persists state; KV cache for the current inference step lives in GPU memory while full history resides in CPU memory or Redis.
KV cache reuse across steps is essential. For a 50-step task with 100K total tokens, naively each step requires a full prefill. KV cache reuse (recomputing only the latest messages) reduces per-step prefill time by 60-80 percent. NVIDIA TensorRT-LLM and SGLang both support partial KV cache reuse.
B200 AND THE VALUE OF LARGER MEMORY FOR AGENTS
The B200's 192 GB VRAM holds 16 concurrent 8K-context agent sessions (70B model at FP16 KV cache), versus 4 on H100. This 4x increase in concurrent agent capacity directly reduces inter-node communication and improves opportunistic batching across agents on the same GPU.
For multi-agent deployments, a single 8-GPU B200 node hosts 4x 70B model instances and supports 128 concurrent agent sessions. Equivalent on H100 requires 4-5x as many nodes. The TCO advantage for agent workloads is estimated at 2.5-3.5x versus H100 when accounting for networking and management overhead.