How Many Parallel Agents Can Your RAM Handle? KV Cache Caps You First

Run a few AI agents in parallel on a local machine and the wall you hit first is almost never the model's weight size. It is the KV cache: the per-agent memory that grows with every token of context each agent accumulates. The model loads into RAM once and is shared, but each running agent carries its own state that keeps swelling until memory runs out. Plan around the cache and you fit more agents on the same box.

Quick Answer

Your model weights load once and are shared across every agent, so they are a fixed cost. What scales with the number of parallel agents is the KV cache, the attention memory that grows with each agent's context length. On a 128GB machine, trimming each agent's context window from 32K to 8K tokens can roughly quadruple how many agents fit, because the cache shrinks with context, not with model size.

Why the Cache, Not the Model, Caps You

When you load a local model, its weights occupy a fixed block of memory whether one agent or twenty are using it. Spin up a second agent and you do not load a second copy of the model. What you do add is a second KV cache: the stored keys and values the attention mechanism needs to avoid recomputing the entire conversation on every new token.

That cache grows linearly with context length. A short prompt costs little; an agent reasoning across a long task accumulates a large cache. Run five agents and you hold five independent caches that all keep growing. The shared weights stayed put, but the caches climbed until the machine ran out of room, and that is the practical ceiling on local parallel agents. The trap is intuition built around model size: people ask whether a 70B or a 32B model lets them run more agents, when the more useful question is how long each agent's context is allowed to get.

A Rough Way to Estimate Capacity

Think in three buckets. The model weights take a fixed amount, depending on the model and its precision. The operating system and your application take a slice. Whatever is left is your KV cache budget, and that budget is what you divide among agents.

Because cache size scales with context length, halving the context per agent roughly doubles how many agents fit in the same budget. As a reference point, public testing on a 10GB-class cache budget fit only a handful of agents at 8K context in full precision, and fewer still at longer contexts. The exact numbers depend on the model and your setup, but the relationship holds: shorter contexts, more agents. A machine built for this work needs generous, fast memory, which is why the AI PC range at Evetech leans on high RAM capacity rather than headline core counts alone.

Practical Ways to Fit More Agents

The cleanest lever is context discipline. Give each agent only the context it needs rather than a blanket large window; a summariser does not need 32K tokens of history. Beyond that, quantising the cache to a lower precision shrinks its footprint, and offloading inactive agents' caches to system RAM or SSD frees the fastest memory for whoever is actively generating. The headline upgrade, though, is simply more RAM, since the cache budget is whatever is left after the weights load. Builders comparing memory-heavy configurations often start from the PC best sellers at Evetech to see which platforms carry the capacity these workloads demand.

Frequently Asked Questions

Does a bigger model mean fewer parallel agents?

A bigger model uses more fixed memory for its weights, which leaves less budget for caches, so indirectly yes. But within a given model, the number of agents you can run is governed mainly by each agent's context length, not the weight size.

What is the KV cache in plain terms?

It is the attention memory a model keeps so it does not have to reprocess the whole conversation for every new token. It is stored per agent and grows as each agent's context gets longer.

Why does the model only load once?

Parallel agents share the same loaded weights in memory rather than each loading a private copy. That is why weights are a one-time cost and the caches are the part that multiplies with agents.

How do I get more agents on the same machine?

Shorten each agent's context window, quantise the cache, offload idle caches off the fast memory, or add RAM. Context discipline is usually the highest-impact change and costs nothing.