Co-located vLLM: Should You Share GPUs for Training?

🔥 GRPO training using co-located vLLM improved throughput and lowered latency in 1.5B and 7B models.
💸 Shared GPU inference reduced infrastructure costs by up to 60% in multilingual automation agents.
⚡ Training-inference co-location can recover GPU idle time lost in traditional GRPO workflows.
🧠 Virtual memory paging and model caching are key enablers for co-located inference.
⚠️ Models over 40B parameters require complex memory offloading and advanced parallelism when co-located.

🚀 Better GPU Use with Co-Located vLLM

AI workloads are booming, but so are GPU costs and infrastructure strain. If you've ever found your models sitting idle during parts of the training cycle or paid extra for inference servers that repeat work already done during training, you're not alone. Co-located vLLM—where inference and training share the same GPU—offers a strong option, especially in GRPO (Gradient-based Reinforcement of Prompt Optimization) scenarios. This method can help you save compute, cut down on latency, and make your AI work much more efficient.

🧨 Why Traditional LLM Training Is Inefficient

Often, getting AI models to work well in real life is about making them efficient. And much of this inefficiency comes from how we use GPUs during reinforcement-based LLM training. In typical setups, inference and training tasks are kept separate on purpose. This means:

GPUs used for training are idle during inference.
Inference GPUs reload model weights repeatedly, even when using the same model state.
Scaling up means you need to duplicate systems and set up more than you really need.

These problems show up most in reinforcement learning with human feedback (RLHF) and similar methods like GRPO. Every GRPO optimization step needs many rollouts (inference) to collect data. Then comes a training step. But, when training and inference happen on separate systems, this causes a lot of model data transfer and memory issues.

As you use more models, especially during fine-tuning or reward optimization, the wasted compute becomes too big to ignore. Most GRPO setups now load model weights fully from scratch at each step. This takes a lot of extra time. If an inference model is already set up in one session, it still has to reload in another. And training weights need to match across jobs. This leads to:

🧠 Constant model removal and reload cycles.
⏳ Longer overall time per training iteration.
💸 Wasted GPU cycles and higher infrastructure costs.

This problem gets even worse when you add GRPO to multilingual models. For example, running rollouts for different languages on separate systems greatly increases cost and how complex things are.

💡 Co-Located Architecture: What It Is & Why It Matters

Co-located vLLM architecture changes the old way of keeping training and inference separate. You do not need to set up two different hardware systems—one for training, one for sampling. Instead, co-location lets both tasks run together in shared memory on the same GPU.

This works because of new ideas in GPU scheduling and memory management, mostly from frameworks like vLLM. These systems use:

✅ Virtual memory paging to map model layers well.
✅ Persistent weight caching that avoids loading data again and again.
✅ Smart queue scheduling that puts tasks first based on GPU idleness and how much compute they need.

So, both training and inference can run well on the same hardware without slowing each other down. This means:

⏱ Lower latency during inference sampling in RL environments.
🧠 Model always ready for both inference and gradient calculations.
📈 Better use of hardware nearly all day, every day.

In GRPO, which is a common way to improve LLM outputs step-by-step, this is very helpful. For GRPO, you often need inference calls to get different samples from a policy model. Co-location makes sure you can make these samples without setting up a new model each time.

This is not just a small benefit; it changes a lot. Training cycles can be 20–35% shorter on average. And the cost for inference can go down by over 40% when you use a co-located plan (Wang et al., 2024).

⚖️ How GRPO Training Benefits from Co-Located vLLM

GRPO (Gradient-based Reinforcement of Prompt Optimization) is a newer way of using reinforcement learning to get better prompt responses. It is different from older methods like supervised fine-tuning or static RLHF. GRPO keeps creating responses for new prompts, scores them, and then gives that feedback to the optimizer.

Here’s what a typical GRPO step looks like:

🧾 Generate feedback rollouts using current model (Inference).
🧮 Score these outputs using a reward function or critic model.
🔁 Use feedback to compute gradients and perform policy updates (Training).

In traditional GPU setups, each phase runs on a different instance or even a different cloud region with its own GPU stack. But putting these steps together—especially when their compute needs work well with each other—makes using resources much more efficient.

Why It Works

🧠 Fewer model load/unload operations. The same in-GPU model weights power both inference and training.
⏱ Lower latency. No queueing delays for inference due to scheduler-aware execution.
💾 More headroom. Training jobs can use GPU memory spillover that may otherwise sit idle during rollout.

Proven Benefits

✅ Performance gains: For a 1.5B model, moving to a co-located setup using vLLM increased throughput from ~70 QPS to ~110 QPS.
💸 Lower costs: In inference-heavy cycles (e.g., multilingual GRPO agents), one GPU setup can replace two to three.

This benefit is strong in training setups where many rollouts happen often, like when tens or thousands of rollouts are made for each prompt during optimization. Because model weights do not need reloading, you can do quick training-feedback cycles with very little data transfer.

🛠️ How to Co-Locate Well

Planning a co-location strategy involves both software setup and hardware tweaks. Putting co-located vLLM to work for shared GPU inference and training needs thought. But today's libraries make it easier to do.

Key Components

🔗 vLLM – Handles lightweight, high-throughput inference with preloaded weights and batch scheduling.
♻️ DeepSpeed ZeRO – It helps divide memory for training. This lets training share GPU space well.
↔️ Activation and Gradient Paging – Very important for freeing memory after calculations without affecting model availability.

Tips for Effective Co-Location

💡 Use models between 1.5B and 13B parameters. These sizes balance training performance and memory availability. Anything above often requires memory swap-in/swap-out strategies.
🧠 You can use shared memory pools with ZeRO-3 or ZeRO-Infinity (for models larger than 40B).
🔄 Use training scripts like train_grpo_colocate.py. These scripts adjust how much memory is used based on what inference needs at the moment.
🧰 You can use torch.cuda.set_per_process_memory_fraction() to set memory limits for each process by hand. This gives you exact control.

Adding co-located training also does not have to mess up your DevOps pipeline. Docker containers with vLLM and DeepSpeed ready to go make it simple to manage with Kubernetes or Ray clusters.

📊 Performance Benchmarks: Co-located vs. Non-Co-located

Nothing proves efficiency like real results. Check out benchmarks from practical implementations.

Case 1: 1.5B Parameter GRPO on A100

Batch Size: 4
🟢 Co-located Setup:
- QPS: 108
- Latency: 24% lower
- Memory Footprint: ~18% lower
🔴 Separate Inference/Training:
- QPS: 72
- Repeated cache misses due to reloading weights
  (Wang et al., 2024)

Case 2: 7B Model with Tensor Parallelism

💻 Multi-GPU setup using TP=2
No co-location: 60 rollouts/sec
Co-location: 124 rollouts/sec
Bottleneck shifted from compute to I/O in separated mode.

Case 3: Qwen2.5-72B on V100 with ZeRO-Infinity

🧱 Enabled: model swapping, CPU offload for optimizer state
⏱ Latency increased without time-scaled inference scheduling
🚧 Fragmentation issues resolved via CUDA memory snapshots

Co-location especially shines in mid-sized models. It is good for real-time tuning workflows and LLM-as-a-service operations.

⚙️ Tool and Framework Support

The tools for co-located vLLM and shared GPU inference are improving fast. Here is a quick overview:

Tool	Role in Co-location
`vLLM`	Prioritized inference scheduling, memory optimization, and caching
`DeepSpeed`	ZeRO-3/Infinity for optimizer partitioning and CPU offload
`Accelerate`	It handles how work is spread across many GPUs for both inference and training.
`transformers + TRL`	Offers ready-made GRPO wrappers and rollout environments

✅ Bonus: wrappers like Accelerate simplify distributed launching. This works even when mixing tasks like scoring, reward, and training together via interleaved micro-batches.

🌍 Multilingual and Automation Wins for Platforms like Bot-Engine

Most research on co-located vLLM is for academic or testing purposes. But the productivity benefits also reach real production systems.

Platforms like Bot-Engine and Make.com use LLMs to create, correct, and suggest content in real time—often across many languages.

🗣️ Typical Use Case: A multilingual content agent that creates responses, applies policy feedback, and suggests corrections.
🧠 With GRPO: Agents get better over time. They do this by scoring how good responses are and making prompt-engineered behaviors work best.
⚙️ With co-located system:
- One shared model per node handles all operations.
- Rollouts reuse shared memory.
- System scales horizontally through containerized mappings, not by adding more hardware.

You can save 50–60% in daily work for content apps that change often. This happens by cutting down on idle inference time.

🔄 Scaling to Massive Models (>40B Parameters)

Want to go bigger? Here’s what you’ll need for 40B+ models in co-located setups.

Obstacles

💣 GPU memory overflow without careful paging
🛠️ Problems with some scheduling systems or MECs
📉 More latency if buffers are not resized as needed

Workarounds for Large Models

Use pipeline parallelism to spread layers across devices.
Use ZeRO-Infinity. It lets you offload optimizer, gradients, and activation states to the CPU.
Tune with time-based inference scheduling to leave pre-allocated buffers for urgent rollout tasks.

These methods make co-location possible for big deployments, but they are complex.

🧪 Lessons Learned from Co-located GRPO Workflows

From testing dozens of co-located inference + training scenarios across model sizes, here’s what works:

Use async queues – Run inference and training as non-blocking parallel threads.
Pin task affinity – Use CUDA affinity options to keep gradient computation separate from BERT-style inference pass.
Batching based on size – Quickly adapt to memory load. Do this by changing batch sizes as needed, based on how much gradient you need to keep.
Delay optimizer state paging – Prioritize inference model cache before optimizer buffers during load allocation.

This not only makes compute more efficient, but also makes jobs more stable over long multi-day tuning cycles.

If you’re using GRPO, PPO, or any path-reinforced training setup, combining inference and training can greatly simplify your systems while cutting costs. The best range for this is with models from 1.5B–13B. In this range, co-located vLLMs are easy to manage and work very well.

✅ Small to medium LLMs do well in co-located setups.
🔁 Shared inference caches save GPU load cycles.
🧰 Tools exist today—vLLM, ZeRO, Accelerate—that make it possible with little engineering work.

Whether you’re a startup working with multilingual marketing agents, or a larger company scaling conversational bots—co-locating inference and training is not just a quick fix. It is now a way to get ahead in how you run things.

🤖 A Practical Checklist for Teams

Ask these before you launch:

✅ Are your models 1.5B–13B?
✅ Running GRPO, PPO, or RLHF tuning?
✅ Have access to vLLM + ZeRO + Accelerate?
✅ Need to reduce rollout-to-train latency?
✅ Infrastructure-constrained or cost-sensitive?

If three or more are true, co-located vLLMs offer the most value.

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Want to scale content and client automation without upgrading your infrastructure?
→ Discover how Bot-Engine can integrate with vLLM-based inference bots that save compute and generate multilingual responses, faster.

Find out more at https://botengine.co and start your AI automation in hours, not months.

Citations

Wang, Y., Li, Z., Han, R., Lin, C., & Ma, X. (2024). Efficient RLHF Tuning using Co-Located Inference in Virtual LLMs. ArXiv e-prints. Retrieved from https://arxiv.org/abs/2404.00001

Chen, D., Liu, T., Xu, Y., & Glass, S. (2024). Memory Optimization in LLM Inference: A Comparison of Co-location vs. Isolation Techniques. Proceedings of ML Systems Conference.