Multi-Instance Processing (MIP)

• Multi-Instance Processing (MIP) is a paradigm that partitions a single compute entity, like a GPU or LLM prompt, into isolated instances to concurrently execute independent workloads.
• It leverages hardware techniques such as NVIDIA’s MIG and software strategies in LLM inference to optimize resource allocation, ensure tenant isolation, and reduce latency.
• Empirical studies show that MIP improves throughput and efficiency while balancing batch size constraints and scheduling challenges in heterogeneous computing environments.

Multi-Instance Processing (MIP) is a paradigm for subdividing a single compute entity—such as a GPU or a model prompt—into multiple, resource-isolated “instances” that can concurrently execute independent workloads. MIP has become fundamental in both hardware-level heterogeneous computing, notably within datacenter GPUs via NVIDIA’s Multi-Instance GPU (MIG) technology, and in software-level inference contexts such as LLMs, where a single model processes multiple instances per invocation. These developments address the imperative for improved hardware utilization, fine-grained multi-tenancy, and scalable resource allocation under stringent performance, cost, and isolation constraints.

1. MIP Concepts and Formal Definitions

MIP enables simultaneous execution of multiple workloads on a shared substrate, with distinct formalizations emerging from hardware and inference-driven contexts. On NVIDIA GPUs, MIG provides hardware-enforced instance isolation: a single physical device can be carved into up to seven “slices,” each with dedicated streaming multiprocessors (SMs), memory, cache, and I/O bandwidth, ensuring deterministic resource allocation and eliminating cross-tenant interference (Zhang et al., 2023, Li et al., 2022, Villarrubia et al., 18 Jul 2025, Turkkan et al., 2024). In LLM processing, MIP refers to multi-instance inference where an LLM receives a set of $n>1$ examples in a single prompt and produces aggregated results (Chen et al., 23 Mar 2026).

For LLM MIP, the formal setting is:

• Instance set $X'=\{x_1,\ldots, x_n\}$ sampled from master pool $X$.
• Instruction prompt $\tau$.
• Model $M_\theta$ producing output $o\sim p_\theta(o\,|\,\tau,X')$, with accuracy metrics defined over aggregate and per-instance outputs.

For MIG-enabled systems, instance allocation and scheduling are modeled as multi-dimensional bin-packing and makespan minimization problems, subject to instance size, memory, and index constraints (Turkkan et al., 2024, Villarrubia et al., 18 Jul 2025).

2. Hardware-Level MIP: Multi-Instance GPU (MIG) Architecture

MIG technology (§ 1 in (Zhang et al., 2023, Villarrubia et al., 18 Jul 2025)) implements MIP by partitioning the GPU into logical instances (“slices”) with strict guarantees:

• Each instance receives its own SMs, L2 cache, DRAM, and NVLink lanes.
• Partitioning is hardware-enforced; legal combinations are constrained (e.g., A100 supports 1g.5 GB–7g.40 GB, with 18 legal ways to combine them (Li et al., 2022, Zhang et al., 2023)).
• Dynamic reconfiguration allows instance creation/destruction at runtime, with operation latency $O(100\,\mathrm{ms})$ to seconds (see Table 1 in (Villarrubia et al., 18 Jul 2025)).

Resource partitioning in this regime is:

• Instance isolation: no two instances share compute, memory, or cache.
• Valid partitions: concurrent instances cover a disjoint set of slices; partitioning must respect device constraints.
• Scheduling objectives include minimizing aggregate makespan, maximizing throughput, and optimizing slice utilization.

The table below summarizes key characteristics of MIG-based hardware MIP:

Feature	Detail/Constraint	Source
Partition granularity	Up to 7 slices per A100; each slice = "instance"	(Zhang et al., 2023, Li et al., 2022)
Isolation	SMs, L2, DRAM, NVLink are partition-private	(Zhang et al., 2023)
Dynamic reconfig	Create/destroy instance: 0.16–0.42s (A100/H100)	(Villarrubia et al., 18 Jul 2025)
Validity	Legal set of partition footprints only; index/size	(Turkkan et al., 2024)

3. Scheduling, Resource Allocation, and Optimization

MIP on GPUs introduces a distinct scheduling problem, requiring workloads to be packed into fixed-size, non-overlapping instances with both compute- and memory-slice dimensions. These constraints motivate the use of moldable and rigid scheduling paradigms:

• Moldable scheduling: Task $T_i$ can run on any allowed instance size $s$, with execution time $t_i(s)$ determined empirically; scheduler jointly determines $X'=\{x_1,\ldots, x_n\}$0 with $X'=\{x_1,\ldots, x_n\}$1 the allocated slice count (Villarrubia et al., 18 Jul 2025).
• FAR algorithm: A three-phase scheduling algorithm—allocation family generation, LPT+List Scheduling using a partition tree structure, and local search via move/swap refinement. FAR achieves a $X'=\{x_1,\ldots, x_n\}$2 approximation ratio for A100/H100 and $X'=\{x_1,\ldots, x_n\}$3 empirical makespan error on real benchmarks (Villarrubia et al., 18 Jul 2025).
• Placement optimization: Formalized as a two-dimensional bin-packing MILP, with variables for workload-to-GPU assignment, slack/waste accounting, and penalties for migration or repartitioning (Turkkan et al., 2024). A heuristic decomposition targets initial deployment, compaction, and full reshuffle steps.

Notably, reconfiguration overhead for instance changes is negligible if tasks are seconds–minutes in duration (Villarrubia et al., 18 Jul 2025).

4. Performance, Benchmarking, and Empirical Insights

Benchmark analyses (Zhang et al., 2023, Li et al., 2022, Villarrubia et al., 18 Jul 2025, Turkkan et al., 2024) provide detailed empirical evaluation of MIP platforms:

• Training and inference: Small instances (e.g., 1g) saturate throughput at moderate batch sizes, while large instances under-utilize SMs unless batch size is large. Larger instances are more energy efficient per sample.
• Latency and isolation: MIG’s hardware isolation yields deterministic tail-latency (P99/P99.9) and eliminates multi-tenant jitter seen with software-only sharing (e.g., MPS). For small batch inference, MPS and MIG have comparable average latency, but MIG shows up to 10–20× lower P99 jitter (Zhang et al., 2023).
• Overhead: MISO profiling (hybrid MPS→MIG) adds $X'=\{x_1,\ldots, x_n\}$4 overhead due to software partitioning and $X'=\{x_1,\ldots, x_n\}$5 checkpointing, yet reduces job completion times by $X'=\{x_1,\ldots, x_n\}$6 vs. unpartitioned execution and $X'=\{x_1,\ldots, x_n\}$7 vs. best static-MIG partition (Li et al., 2022).
• Placement efficiency: Joint MIP formulation provides up to $X'=\{x_1,\ldots, x_n\}$8 reduction in GPU consumption and $X'=\{x_1,\ldots, x_n\}$9 decrease in slice wastage relative to load-balanced first-fit heuristics (Turkkan et al., 2024).

5. MIP in LLM Inference: Aggregation and Degradation Dynamics

In the software context, LLM MIP tasks require a model to process multiple instances within a single prompt and aggregate their outputs. Rigorous evaluation with 16 state-of-the-art LLMs across eight aggregation tasks reveals characteristic behaviors (Chen et al., 23 Mar 2026):

• Success Rate (SR) Scaling: SR remains high ($X$0) for $X$1 instances but falls to $X$2–$X$3 by $X$4, and collapses below $X$5 for $X$6.
• Degradation drivers: Instance count $X$7 dominates over aggregate prompt length $X$8 (Spearman $X$9, vs. $\tau$0). Artificially increasing $\tau$1 at fixed $\tau$2 yields negligible additional error.
• Error modes: Failures include individual mistakes, aggregation mistakes, parsing errors, and overlong outputs. At large $\tau$3, invalid outputs and aggregation pathologies rise substantially.
• Recommendations: Batch size $\tau$4 should be capped at $\tau$5–$\tau$6 to avoid collapse. Decompose large input sets agentically (map/reduce) and prompt for per-instance labels. Training LLMs on explicit multi-instance objectives is suggested for improved robustness.

6. Systems Integration, Orchestration, and Open Problems

The integration of MIP into production environments necessitates architectural frameworks spanning placement recommendation, migration orchestration, and non-disruptive partitioning:

• Placement frameworks: Three-component stacks with Placement Recommenders, Migration Planners, and Executor/Actuators using Kubernetes Dynamic Resource Allocation enable seamless instance reshuffling (Turkkan et al., 2024).
• Framework compatibility: Mainstream DL frameworks typically expose only one MIG, requiring per-instance containerization and deployment friction (Zhang et al., 2023).
• Open directions: Dynamic, energy-aware partitioning, hybrid MIG/MPS scheduling, native multi-GI framework support, and calibration of live migration costs represent active areas for research and development (Zhang et al., 2023, Villarrubia et al., 18 Jul 2025).

7. Synthesis of Practical Guidelines and Research Directions

Multi-Instance Processing enables substantial improvements in resource efficiency, isolation, and cost across both hardware and software contexts.

Key actionable insights:

• Favor hardware instance partitioning (MIG) for deterministic isolation on shared GPUs, particularly where tail-latency and performance isolation are critical (Zhang et al., 2023, Li et al., 2022).
• Use dynamic, moldable scheduling to adapt instance configurations to bursty or heterogeneous load profiles (Villarrubia et al., 18 Jul 2025).
• In LLM MIP, strictly constrain per-invocation batch size and utilize structured aggregation or post-processing to maintain high accuracy (Chen et al., 23 Mar 2026).
• Leverage predictive modeling and heuristic scheduling for dynamic workload placement, balancing GPU count, migration cost, and resource wastage (Turkkan et al., 2024).
• Anticipate ongoing developments in multi-tenant orchestration, online scheduling with precedence/QoS constraints, and fine-tuned LLM optimization for distributed repeated reasoning.

MIP will remain central to scalable deep learning, multi-tenant inferencing, and next-generation hardware/software codesign for AI infrastructure.