Agentic Inference NPUs

• Agentic Inference NPUs are specialized processing units engineered for LLM inference in dynamic, long-context agentic scenarios.
• They employ innovations such as flattened systolic arrays, native FlashAttention support, and asymmetric quantization to optimize performance and energy use.
• Quantitative metrics reveal up to 8.5× speedup and significant energy reduction, driven by a co-designed memory hierarchy and custom scheduling strategies.

Agentic Inference NPUs are a new class of neural processing units architected and scheduled specifically for inference serving of LLMs in agentic scenarios—namely, workloads demanding long-context reasoning, tool use, and external environment interaction. Their design is shaped by the unique dual-phase (prefill and decode) memory, compute, and software requirements encountered in agentic LLM deployments, distinct from conventional chatbot- or batch-inference accelerators. Optimization for high utilization, minimal latency, efficient energy use, and sustained throughput across widely variable context lengths and workloads is central to this category.

1. Defining Agentic Inference Workloads

Agentic inference encompasses LLM-powered agents that operate with complex, long, and evolving contexts—requirements often encountered in web browsing, command-line automation, and multi-turn tool call trajectories. These workloads introduce fundamentally different hardware demands compared to chatbot-focused inference:

• Prefill Phase: Processes very large context windows (50k–100k+ tokens), induces massive activation and weight movement through the memory subsystem, and exercises the full model in one pass, leading to working sets of 100–200 GB with bandwidth needs exceeding 2 TB/s for contemporary large-scale models at high utilization (Wu et al., 17 Apr 2026).
• Decode Phase: Characterized by step-by-step, autoregressive token generation where the working set is dominated by growing KV-cache, typically requiring 40–60 GB of capacity and 200–500 GB/s bandwidth. Latency targets are stringent (sub-100 μs per token), demanding careful memory and compute orchestration.

Workloads are further complicated by a mix of reactive (hard-real-time, user-triggered) and proactive (background, best-effort) inference streams and by the need to orchestrate tool calls, sparse retrieval, and structured context inputs (e.g., tile-by-tile DOM parsing) (Wu et al., 11 Sep 2025, Wei et al., 30 Jun 2025).

2. Architectural Innovations in Agentic Inference NPUs

2.1 Flattened Systolic Array Design

Traditional square systolic arrays are ill-suited for the “fat GEMM” patterns prevalent in LLM inference with long contexts (where batch size M ≪ K, N). Agentic NPUs such as PLENA employ a flattened systolic array: $P=M$ processing elements arranged in a single row, each handling an $MLEN$-length hidden-dimension slice. This ensures high utilization and minimizes compute underutilization endemic to conventional array designs (Wu et al., 11 Sep 2025).

2.2 Native FlashAttention Support

Agentic NPUs support on-chip, tile-wise computation of attention via:

• Multi-bank Matrix SRAM with lightweight address-remap for efficient on-chip transposed views.
• Tiled attention fusion in SRAM, avoiding excessive off-chip I/O.
• Custom ISA-level primitives for in-line reduction and normalization (e.g., $V\_RED\_MAX$, $V\_EXP\_V$).

2.3 Asymmetric Quantization and Dataflow

Weights and KV-cache are quantized at low bit-width (e.g., MXINT4/MXINT8) with block-wise power-of-two scaling, and activations use higher-precision on-chip minifloat. Selective fast Hadamard rotations further mitigate quantization error in blocks with severe activation outliers. Quantization schedules and locations are embedded in hardware-exposed instructions (Wu et al., 11 Sep 2025).

2.4 Heterogeneous Memory Hierarchies

Designs span multi-tier, heterogeneous memory systems: on-chip SRAM for ultra-fast buffering, off-chip HBM3E/HBM4 for bandwidth- and capacity-optimized storage, and, where required, outer LPDDR, GDDR, or HBF (high-bandwidth flash) for massive runtime KV-cache or tool call index overflow (Wu et al., 17 Apr 2026). Unified abstraction models fractional data placement, effective bandwidth, and cross-level transfer latency.

Technology	Latency	Bandwidth	Per-Stack Capacity	Use-case
On-chip SRAM	~1.5 ns	4 TB/s	0.256 GB	Working buffers, attention fusion
HBM4	100 ns	2 TB/s	36 GB	Main weights, KV-cache
HBF	1 μs	1 TB/s	384 GB	Rarely accessed weights/tool history

Capacity, power, and bandwidth tradeoffs at every layer must be jointly considered during design-space exploration.

3. Compiler and Scheduling Techniques

3.1 Custom ISA and Tiled Execution

Agentic NPUs expose tiled GEMM, vector reductions, prefetch, and domain-specific primitives (“S_TOOLCALL,” “M_DOMPARSE”) in custom instruction sets. Compilers break input sequences into tiles, schedule double-buffered HBM prefetch for minimizing latency, and fuse FlashAttention and non-GEMM ops at tile granularity.

3.2 Agentic Serving on Heterogeneous SoC

Serving stacks such as Agent.xpu orchestrate CPU (control/outlier kernels), iGPU (dynamic-shape decode and self-attention), and NPU (static-shape prefill GEMMs) in consumer SoCs. Key features include:

• Offline profiling: Constructs an execution graph with fused/chunked kernels, annotated by accelerator affinity, profiled via predicted latency and bandwidth (Wei et al., 30 Jun 2025).
• Predictive, affinity-guided mapping: Assigns each kernel to the appropriate accelerator under memory and power constraints, optimized via cost functions over predicted latencies.
• Fine-grained, kernel-level preemption: Maintains separate real-time and best-effort queues; preemptive scheduling at kernel boundaries ensures reactive latency SLOs are met (TTFT + TPOT minimized).
• Slack-aware and bandwidth-aware dispatch: Opportunistically schedules background tasks to fill idle cycles (“backfill”), while memory-pressure heuristics dynamically throttle concurrency to avoid bandwidth saturation.

4. Memory System Synthesis and Co-Design

The memory-technology design space (spanning on-chip SRAM, HBM, LPDDR, GDDR, and HBF) is co-explored alongside NPU dimensions (systolic array sizing, quantization, prefetch depth) in frameworks such as MemExplorer (Wu et al., 17 Apr 2026). The optimization objectives include maximizing (weighted) throughput across both inference phases and/or minimizing query energy, all under power/area/capacity constraints.

The unified abstraction models:

• Tiered placement ($\alpha_i$ fractions at each level $i$)
• Bandwidth-limited and transfer-latency-overlapped data movement
• Dynamic and static energy costs per tier
• Constraints on effective bandwidth ($B_i^{\mathrm{eff}}$), area, and background power.

This systematic co-optimization yields Pareto-optimal NPU + memory architectures, balancing prefill throughput, decode latency, capacity, energy, and cost.

5. Quantitative Performance and Utilization Metrics

Simulation results for agentic NPU designs such as PLENA (under LLaMA-3.3-70B, $N=8192$) demonstrate substantial gains over prevailing accelerators:

• Standard workload (1k context + 128 gen):
  • PLENA: $4.19$ ms/token ($238$ t/s), A100: $MLEN$0 ms/token ($MLEN$1 t/s) $MLEN$2 speedup
  • TPU v6e: $MLEN$3 ms/token ($MLEN$4 t/s); PLENA yields $MLEN$5 speedup
• Agentic long-context (5.6k + 85k):
  • PLENA: $MLEN$6 ms/token ($MLEN$7 t/s), A100: $MLEN$8 ms/token ($MLEN$9 t/s)
  • Up to $V\_RED\_MAX$0 FLOPs/mm² utilization boost (W4A4KV4 config) in fat-sequence regime ($V\_RED\_MAX$1) (Wu et al., 11 Sep 2025).

For heterogeneous SoCs, Agent.xpu achieves a $V\_RED\_MAX$2 reduction in reactive latency and 1.6–6.8$V\_RED\_MAX$3 higher proactive throughput compared to CPU-only baselines, with up to 30% lower energy/token by routing static workloads to the NPU (Wei et al., 30 Jun 2025).

MemExplorer demonstrates up to $V\_RED\_MAX$4 energy efficiency and $V\_RED\_MAX$5 power efficiency against leading NPUs and up to $V\_RED\_MAX$6 and $V\_RED\_MAX$7 over H100, respectively, in prefill and decode scenarios (Wu et al., 17 Apr 2026).

6. Design Guidelines and Co-Optimization Principles

• Flattened array, block-quantization, minifloat activation: Maximizes utilization and accuracy at low bit-widths; block-wise clipping and selective rotation control quantization error (<0.05 PPL degradation).
• On-chip buffering and double-buffered streaming: Hides HBM latency; capacity can be extended linearly by reducing KV bit-width (e.g., 4 bits for 4$V\_RED\_MAX$8 context).
• ISA/compiler specialization: Exposes prefetch, tiled execution, reductions, and domain-specific agentic primitives at token-group level to manage complex sequence-to-tool workflows.
• Memory system balancing: Mixes SRAM, HBM, and capacity flash to match prefill/decode bandwidth, latency, and working-set demands per phase.
• Serving stack integration: Schedulers must reconcile latency SLOs for reactive tasks with high sustained throughput of background tasks, using context switching, backfill strategies, and real-time memory-pressure feedback.

A plausible implication is that agentic inference NPU research will continue to drive architectural and scheduling innovation in response to the non-uniform, heterogeneous demands of LLM-based agents in production environments.

7. Broader Implications and Future Directions

Agentic inference NPUs represent a paradigm shift in accelerator architectures for LLMs, compelled by the convergence of agent-oriented AI applications and the limits of memory-bandwidth utilization. Industry trends are moving toward:

• Heterogeneous, multi-device NPU systems (e.g., NVIDIA Vera Rubin), each optimized for distinct inference phases and memory technology stacks.
• Co-synthesis tools (e.g., MemExplorer) for rapid, principled design-space exploration under practical constraints.
• Open-source hardware-software co-design stacks (e.g., PLENA) enabling community-driven iteration and validation.

Future research will likely focus on cross-phase memory placement, unified serving abstractions that span from data center to on-device agentic workloads, and integration of NPU-centric agentic accelerators within broader heterogeneous computing environments (Wu et al., 11 Sep 2025, Wu et al., 17 Apr 2026, Wei et al., 30 Jun 2025).