Memory Planning to Avoid OOM in Large Language Model Inference

Running a large language model on a single GPU used to be a dream. Now, it’s a daily challenge. If you’ve ever seen that dreaded Out-of-Memory (OOM) error while trying to infer with a 13B or 20B parameter model, you know how frustrating it is. You’ve got the hardware, you’ve got the model, but the system crashes before it even finishes processing a single prompt. The problem isn’t your GPU-it’s how the model uses memory. And that’s where memory planning comes in.

Why OOM Errors Happen in LLM Inference

It’s not just about model size. A 7B model can crash on a 24GB GPU if the input is long enough. The real culprit is the transformer’s self-attention mechanism. For every token in your input, the model calculates attention scores with every other token. That means for a 4,096-token prompt, it’s doing over 16 million calculations-just for attention. And each of those scores needs to be stored in memory. The memory usage grows with the square of the input length: O(n²). That’s why a 10,000-token context can use 5x more memory than a 2,000-token one.

Traditional fixes like model quantization (going from 16-bit to 4-bit weights) help, but they’re like putting a bandage on a broken bone. You save memory, sure-often 2x to 4x-but you lose accuracy. And in production, even a 5% drop in reasoning quality can mean failed customer interactions or bad summarizations. That’s why smarter memory planning is replacing simple quantization as the go-to strategy.

What Memory Planning Actually Does

Memory planning isn’t about squeezing more out of your GPU. It’s about changing how the model thinks. Instead of storing every attention score from every token, it remembers only what matters. Think of it like human memory: you don’t recall every word someone said-you remember the key points, the tone, the intent. Modern techniques mimic this.

Take CAMELoT is a memory-augmented architecture developed by IBM Research that enhances transformer models with an associative memory module to retain critical context while discarding redundant tokens. Also known as Consolidated Associative Memory Enhanced Long Transformer, it was introduced in 2023 and has been shown to reduce memory usage by up to 55% while improving perplexity by 30% on Llama 2-7b models.. CAMELoT doesn’t just cut tokens-it learns which ones to keep based on three principles: consolidation (reinforcing important patterns), novelty (highlighting new information), and recency (prioritizing recent context). It works like a mental highlighter, tagging what’s worth keeping and letting the rest fade.

Another approach, Dynamic Memory Sparsification (DMS) is a technique developed by the University of Edinburgh that selectively removes less important tokens during inference, with a delay mechanism to transfer their value to remaining tokens before deletion. First published in 2023, DMS has demonstrated an average 47% memory reduction across multiple LLMs with only 0.8% accuracy loss on GLUE benchmarks., works by delaying token removal. It doesn’t delete a token the moment it’s deemed less relevant. Instead, it waits-just long enough-for its information to be absorbed by other tokens. It’s like letting a note transfer its key points to a sticky note before tearing it up. This small delay cuts memory use by 40-50% without hurting accuracy.

Then there’s Larimar is an external episodic memory system from IBM Research that allows real-time updates to a model’s memory during inference without retraining, reducing memory leakage by 92% in adversarial tests. Introduced in 2023, Larimar enables models to remember or forget facts on the fly, making it ideal for dynamic applications like customer service bots.. Unlike CAMELoT and DMS, which work inside the model, Larimar adds an external memory module-like a notebook the model can flip through. You can tell it: "Forget the old address," or "Add this new policy rule." It updates instantly. And because it’s separate, it doesn’t bloat the model’s main memory. One engineer on Reddit said it let them run a 20B model on a single A100 40GB card instead of needing two GPUs.

How These Techniques Compare

Not all memory planning is equal. Here’s how they stack up:

Quantization is easy but brittle. DMS is powerful but adds latency. CAMELoT gives you better accuracy with less memory-perfect for enterprise use cases where output quality matters. Larimar shines when your model needs to adapt on the fly, like a support bot that learns new product details during a conversation.

Real-World Results

Engineers are already seeing results. In a March 2025 GitHub issue, a user reported reducing a 13B model’s memory footprint from 26GB to 15GB using DMS-without losing performance on summarization tasks. Another team at a fintech startup used CAMELoT to run a 70B model on two A100s instead of four, cutting cloud costs by 40%. Reddit threads from January 2025 show users running Llama 3 8B on 24GB consumer cards with 50% less memory usage after adding memory sparsification.

But it’s not all smooth sailing. One Stack Overflow post from November 2024 got 87 upvotes because integrating CAMELoT into an existing pipeline took three weeks. Documentation was sparse. The model had to be recompiled. Custom hooks had to be written. That’s the trade-off: you gain efficiency, but you pay in engineering time.

When to Use What

Here’s a simple decision tree:

• If you’re running a model under 7B parameters and need a quick fix → Use 4-bit quantization. It’s fast, and the accuracy drop is acceptable.
• If your prompts are long (over 4,000 tokens) and you care about accuracy → Go with CAMELoT. It improves reasoning and cuts memory.
• If you need your model to learn new facts during inference (e.g., updating customer records) → Use Larimar. It’s the only one that lets you edit memory on the fly.
• If you’re on a budget and want to squeeze more out of a 24GB card → Try Dynamic Memory Sparsification. It’s hardware-agnostic and works with Hugging Face models.

Many teams combine techniques. One AI team at a healthcare startup uses 4-bit quantization on weights and DMS on activations. They got 60% memory reduction and kept 98% of their original accuracy. That’s the sweet spot.

What’s Coming Next

IBM Research released CAMELoT 2.0 in January 2026, cutting memory use another 15% while boosting long-context reasoning. The University of Edinburgh plans to open-source DMS in Q2 2026, making it accessible to anyone with a Hugging Face setup. Analysts at Forrester predict that by 2028, all major foundation models will include memory optimization as a built-in feature-not an add-on.

Right now, only 37% of enterprises use these techniques. But Gartner says that’ll jump to 70% by 2026. Why? Because scaling models up is no longer sustainable. The future isn’t bigger models-it’s smarter memory.

Getting Started

You don’t need a PhD to start. Here’s how to begin:

1. Measure your current memory usage. Use torch.cuda.memory_summary() in PyTorch or nvidia-smi during inference.
2. Try quantization first. If accuracy drops too much, move on.
3. For long contexts, test DMS using the open-source Hugging Face transformers library with memory sparsification enabled.
4. If you need dynamic updates, experiment with Larimar’s external memory API (available via IBM’s GitHub).
5. Monitor latency. Aggressive sparsification can add 10-20% to response times. Find your balance.

Start small. Run a 7B model on your local GPU with DMS. Compare memory use and speed. You’ll be surprised how much you can squeeze out without buying new hardware.

Final Thoughts

You don’t need the biggest GPU to run the biggest model. You need the smartest memory strategy. The days of throwing more hardware at the problem are over. The future belongs to those who understand how models remember-and how to help them forget what doesn’t matter. Start small. Test one technique. Measure the difference. And remember: efficiency isn’t a compromise. It’s the new way to scale.