• An LLM serving interactive inference

• An embedding pipeline powering semantic search

• OCR or document AI jobs

• Fine-tuning or continual learning

• Background evaluation or batching tasks

And with the rise of compute marketplaces, GPU-sharing platforms, and custom scheduling systems, you can even rent out your idle GPU time to solve compute jobs and earn revenue in platforms like Salad.

This operational model only works efficiently if GPUs are used to their fullest potential — and that’s where VRAM partitioning becomes crucial.

Before we dive into the technical methods, let’s look at a real-world example that demonstrates why VRAM partitioning is not just useful, but economically important.

1. A Real-World Example: How VRAM Partitioning Directly Impacts Utilization and Revenue

Consider a setup where unused GPU systems are rented out to run medium-heavy compute jobs such as medium-scale training, document processing, or multistep inference pipelines.

Consider your infrastructure has three machines:

System A — High-End

• Very powerful GPU

• 80GB VRAM

• Designed for extremely large models or heavy workloads

System B — Mid-Tier

• Same GPU FLOPs as System C

• 40GB VRAM

• High system RAM

• Capable of running two medium-heavy jobs in parallel

System C — Entry-Level

• Same GPU FLOPs as System B

• 20GB VRAM

• Suitable for one medium-heavy job

What Happens Without VRAM Partitioning

When these systems are rented out:

• Each system receives one job

• System B receives the same type of medium-heavy job as System C

• Yet System B’s extra VRAM and system RAM stay unused

• In terms of job handling and revenue, System B behaves exactly like System C

System B’s potential is wasted because:

1. VRAM is treated as a monolithic block

2. The scheduler cannot safely place two jobs on System B

3. A single job’s memory spike could crash both workloads

Thus, System B earns the same revenue as System C even though it could do more.

What Happens With VRAM Partitioning

When VRAM partitioning is applied:

• System B is effectively split into two logical GPU slices, each with ~20GB VRAM

• Two jobs can run independently without affecting each other

• Resource utilization increases dramatically

• System B becomes equivalent to two System Cs in terms of throughput

• Revenue from System B nearly doubles

This is the key insight:

VRAM partitioning transforms hardware potential into actual throughput and revenue.

This is true whether you're running personal compute rentals, enterprise AI workloads, or shared GPU clusters.

2. Why VRAM Partitioning Is Essential for Modern AI Workloads

This section explains why partitioning is needed — keeping it clean and non-repetitive.

2.1 GPUs Are Now Multi-Model, Multi-Workload Systems

Even on a single machine, AI teams commonly run:

• A main LLM for chat or API inference

• Secondary LLMs for embeddings or retrieval

• OCR / vision models

• Data-processing or vectorization jobs

• Fine-tuning / evaluation tasks

All of these share the GPU's VRAM.

Partitioning ensures each model or pipeline gets guaranteed resources.

2.2 Multi-Tenancy is the New Default

In modern environments:

• Multiple developers

• Multiple microservices

• Multiple users

• Automated pipelines

often share the same GPU.

Partitioning prevents:

• Memory starvation

• Job crashes

• Interference across workloads

2.3 GPUs Lack Memory Virtualization

CPUs have:

• Virtual memory

• Memory isolation

• Swapping

GPUs do not.

If VRAM hits 100%:

• The GPU throws an OOM

• All processes may crash

• The system may become unstable

Partitioning provides a safety boundary.

2.4 LLMs Introduce Dynamic Memory Behavior

LLMs cause fluctuating VRAM usage due to:

• KV-cache growth

• Varying sequence lengths

• Batch merging

• Token streaming

Partitioning ensures one workload’s fluctuations don’t take others down.

3. Why Partitioning Is Hard on Non-MIG GPUs

Many GPUs widely used today do not support NVIDIA MIG, including:

• RTX 3090 / 4090

• L40 / L40S

• AWS T4 (g4dn), A10G (g5), V100 (p3), A100 (p4d without MIG)

• Azure NCasT4_v3, NC6s_v3, ND-series (A100 non-MIG)

These GPUs operate entirely on:

First-come, first-served VRAM allocation with no hardware isolation.

Frameworks like PyTorch/TensorFlow make this even harder because they pre-allocate VRAM aggressively and don’t release it immediately.

Thus, software-based VRAM partitioning is required.

4. Practical VRAM Partitioning Techniques for Non-MIG GPUs

Here are the real, production-tested methods.

4.1 Framework-Level Soft VRAM Limits

PyTorch

torch.cuda.set_per_process_memory_fraction(0.5, 0)

TensorFlow

config.gpu_options.per_process_gpu_memory_fraction = 0.4

Good for guardrails but not enforceable boundaries.

4.2 Logical GPU Shrinking (Environment Variables)

XLA_PYTHON_CLIENT_MEM_FRACTION=0.3

Ideal for notebooks or dev setups.

4.3 NVIDIA MPS (Multi-Process Service)

export CUDA_MPS_ACTIVE_THREAD_PERCENTAGE=40

Improves scheduling fairness and reduces random spikes, but does not provide VRAM isolation.

4.4 OS / Container-Level Memory Boundaries

docker run --memory=16g --gpus '"device=0"' app

Cgroups limit pinned RAM → indirectly enforce VRAM usage.

Most reliable in production clusters (AWS EKS, Azure AKS).

4.5 Custom CUDA Allocators (True VRAM Partitioning)

cudaMalloc(&ptr, 8LL * 1024 * 1024 * 1024);

Used in:

• TensorRT

• DeepSpeed

• vLLM

Provides precise control.

4.6 LLM Serving Framework Memory Schedulers

Frameworks like vLLM, Triton, TGI implement their own partitioning using:

• KV cache pools

• Static memory slabs

• Token schedulers

This is currently the most efficient method for multi-tenant LLM workloads.

5. Monitoring VRAM per Process (Critical)

5.1 Query processes

nvidia-smi --query-compute-apps=pid,process_name,used_gpu_memory --format=csv

5.2 Real-time monitoring

watch -n 1 nvidia-smi

5.3 NVML programmatic approach

import pynvml
...

5.4 Enforcement

if p.usedGpuMemory > MAX_MB * 1024**2:
    kill

6. Summary Table — Choosing the Right Strategy

Conclusion

The real-world example of Systems A, B, and C illustrates a deeper truth:

GPU VRAM partitioning directly translates into stability, fairness, higher utilization, and increased revenue.

In a world where GPUs are expensive and workloads are becoming multi-model and multi-tenant by default, software-driven VRAM partitioning is no longer optional — it is essential infrastructure.