There is no single hardware specification for a self-hosted AI agent. Size the agent runtime and the model-inference service as separate workloads, then account for the model format, context length, concurrent requests, serving configuration, and deployment topology. A lightweight orchestrator may run comfortably on a CPU host while a locally served language model needs substantial RAM or GPU memory. Your final configuration should come from an inventory of those workloads and a representative load test, not from a generic “AI server” number.
What you are actually sizing
A self-hosted agent commonly contains several components: an orchestration process that plans and calls tools, an inference server that generates text, embedding or reranking models, a vector database or other index, source data, and operational services such as containers, logging and monitoring. They may share one machine or be placed on separate hosts or node pools.
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Google Cloud’s Kubernetes tutorial illustrates the separation clearly: the agent application runs on a standard CPU node pool, while a self-hosted Llama 3.1 model served with vLLM runs on a GPU-enabled node pool. That is an architecture example, not a requirement to use Kubernetes or to buy separate machines. It demonstrates why the agent process and inference process should be estimated independently before you decide whether consolidation is sensible.
| Workload | What drives its size | What to measure |
|---|---|---|
| Agent runtime | Framework, tool calls, number of simultaneous sessions, browser or code-execution workers, and state retained in memory | Resident RAM, CPU utilization, process count, request latency |
| Text-generation server | Model weights, precision or quantization, context length, KV-cache allocation, batching and parallelism | RAM or VRAM, cache occupancy, tokens per second, queueing and errors |
| Embeddings and reranking | Model count, model size, batch size and whether models remain loaded | Memory while loaded, CPU or GPU utilization and request bursts |
| Data and indexes | Original documents, transformed chunks, vector indexes, metadata and replication | Disk capacity, growth rate and I/O latency |
| Operations | Container images, logs, caches, temporary files and orchestration overhead | Peak disk usage, inode use, memory and CPU headroom |
Count every component that will coexist at peak load. A model that fits by itself can still fail when the agent, embedding service and index are loaded on the same host.
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Define the deployment before choosing hardware
Choose a topology
Record whether the agent, inference server, embedding model, vector store and data-processing jobs share one host. Also record whether inference is CPU-only or GPU-backed, whether one model or several models stay resident, and whether the system is single-node or distributed.
- Single host: simplest operations and lowest network overhead, but every service competes for the same RAM, CPU, storage and (if present) GPU memory.
- Split hosts: lets the agent use ordinary CPU capacity while the inference host is optimized for accelerator memory. It adds network latency, service discovery and another failure domain.
- Distributed serving: useful for models or throughput targets that exceed one machine, but it introduces scheduler, interconnect, replica and shared-storage requirements. Published distributed manifests must not be treated as small-server baselines.
Write down the workload assumptions
Before calculating capacity, specify the intended model name, model format and precision, maximum and typical context length, expected peak concurrent requests, target response speed, and whether embeddings or other models run alongside generation. These variables determine whether memory capacity, compute throughput, storage or another service becomes the bottleneck.
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The available official guidance does not establish universal numeric assumptions for those values. If you publish or share a sizing worksheet, show the assumptions beside every result; changing context length or concurrency can invalidate an otherwise identical hardware estimate.
How to estimate memory
Start with weights, then add the KV cache
Model weights are only the first part of inference memory. vLLM’s CPU guidance describes the budget as weight shards plus the configured key-value (KV) cache space. The cache grows with context and request load, so a model that fits at a short context or one request may run out of memory when several long conversations are active.
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Use a worksheet rather than a single “model size” figure:
Required host or accelerator memory
= model weights and runtime overhead
+ configured KV-cache capacity
+ other resident models
+ inference and API-process overhead
+ agent, operating-system and service headroomThe weight-file size is not always equal to the memory consumed while serving. Runtime metadata, temporary buffers, allocator fragmentation and the selected precision or quantization add overhead. Obtain the actual values for the model format and serving engine you intend to deploy, then leave room for the non-model processes listed in the equation.
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Longer context windows require more KV-cache space. Concurrent requests consume cache simultaneously, and batching or prefill settings can change the peak. Define both a normal and a worst-case request pattern, such as several users sending long prompts at once, and size for the peak you are willing to support. If you cap context or concurrency in the serving configuration, record those limits because they are part of the capacity plan.
Respect NUMA boundaries on CPU systems
On a multi-socket machine, memory is divided into NUMA nodes. vLLM warns that exceeding the capacity of the NUMA node relevant to a worker can terminate that worker with an out-of-memory error even when the machine appears to have sufficient aggregate RAM. Check where each worker is placed and ensure its weights, cache and runtime allocation fit the applicable node. Tensor-parallel or multi-process configurations make this placement question especially important.
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GPU memory is a separate constraint
For GPU inference, verify that the selected model and runtime fit in usable accelerator memory after accounting for weights, cache, temporary buffers and concurrent requests. The official material supports GPU-backed serving but does not establish a universal accelerator model or VRAM threshold. A GPU with enough capacity for weights can still fail when the configured context or concurrency consumes the remaining memory. If the model does not fit on one accelerator, determine whether the serving engine supports an appropriate multi-GPU arrangement and whether the host interconnect and software versions support it.
Reserve memory for the rest of the stack
On a shared host, add the agent process, embedding and reranking models, vector-store processes, container runtime, operating system and monitoring. Keep explicit headroom for restarts and transient spikes instead of allocating every byte to the model server. On a split deployment, make the same calculation separately for each host; moving the agent away from the GPU server does not remove the need to size its own CPU and RAM.
How to estimate CPU capacity
Separate generation work from request handling
CPU demand comes from more than token generation. The inference workers perform scheduling and preprocessing, while API servers accept connections, serialize requests and return results. The agent may parse tool outputs, execute code, manage multiple conversations or run retrieval. Count all of those processes on the host where they run.
vLLM’s documentation emphasizes that CPU needs depend on the serving configuration. Data parallelism and multiple API servers increase CPU requirements; there is no defensible universal core count. Use the process topology you plan to deploy, then measure it under representative load.
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Use a load-based worksheet
- List the number of inference workers, API servers, embedding workers and agent workers.
- Record whether the model is generated on CPU, GPU or a combination, and which preprocessing stages remain on the CPU.
- Set the expected typical and peak concurrent requests, including tool calls that may create bursts.
- Reserve CPU for the operating system, container runtime, storage services and monitoring.
- Run the exact software versions and configuration while watching per-process CPU, run queues, latency and rejected or queued requests.
High average utilization is not automatically a failure, but sustained saturation leaves no room for bursts and can increase queueing. Conversely, adding cores will not solve a GPU-memory limit or a cache-allocation error.
Consider affinity and NUMA
On multi-socket systems, CPU affinity and memory placement can affect both capacity and stability. Keep a worker’s CPU and memory close to the NUMA node where its model and cache reside when the serving engine supports that arrangement. Validate the placement after deployment; a nominal core count without topology information is incomplete.
How to estimate storage
Inventory every byte that the deployment retains
Storage planning should begin with the actual deployment contents, not a generic “AI model” allowance. Create an inventory with current size, expected growth and retention policy for each item.
| Storage item | Questions to answer |
|---|---|
| Model files | How many models, shards and precision variants must be downloaded or kept for rollback? |
| Source data | Will documents, audio, images or tool datasets be retained locally, and how fast will they grow? |
| Indexes and metadata | How large are vector, keyword and relational indexes after chunking, and do you keep rebuild copies? |
| Caches | Does the runtime cache model downloads, tokenization, compiled kernels or retrieved results? |
| Containers and packages | How many images and old layers remain during upgrades? |
| Logs and traces | What is the daily volume, retention period and rotation policy? |
| Temporary and ephemeral files | Where are uploads, extraction files, checkpoints and failed-job artifacts written? |
Sum the peak values, not only the current directory sizes, and include free-space headroom for downloads, upgrades and index rebuilds. Separate capacity from performance: the cited official sources do not establish a universal SSD model or IOPS target. Choose storage performance after observing model-load time, index operations and log or data-ingest behavior in your workload.
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Interpret large ephemeral-storage figures correctly
The vLLM LeaderWorkerSet guide includes an example that allocates 800 GiB of ephemeral storage per node. That value belongs to a large distributed serving configuration and should not be copied into a home server or a small single-host deployment. It is a reminder to account for temporary space, not a standard requirement.
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The documented vLLM distributed example serves Meta-Llama-3.1-405B-Instruct across a specified multi-node arrangement. It requires at least two Kubernetes nodes, with eight GPUs on each node. Its sample manifest lists the following allocation per pod or node in that configuration:
| Resource | Published allocation | Scope |
|---|---|---|
| CPU | 125 CPUs | Per pod or node allocation in the documented distributed pattern |
| Memory | 1,124 GiB | Per pod or node allocation in that pattern |
| Ephemeral storage | 800 GiB | Per node in that pattern |
| Shared memory volume | 15 GiB | Volume in the sample manifest |
Those figures are configuration values for that model, serving engine and multi-node design. They are not typical requirements for a self-hosted agent, a small local model or a single workstation. No representative organization-published statistic establishes typical CPU, RAM or storage needs across self-hosted agents, so do not present this example as an industry average.
A repeatable sizing procedure
- Describe the topology. Draw the agent, inference server, embedding model, vector store and data paths. Mark which services share a host and whether inference uses CPU or GPU.
- Freeze the model configuration. Record the exact model and revision, file format, precision or quantization, context limit, cache settings and number of simultaneously loaded models.
- Define traffic. Record normal and peak concurrent requests, prompt and output sizes, tool-call bursts, latency goals and any rate limits.
- Calculate memory. Add weights, runtime overhead, KV cache, other resident models and non-model services. Check each NUMA node or accelerator separately where applicable.
- Calculate CPU. Include inference workers, API servers, agent and retrieval processes, embeddings, operating-system reserve and parallelism overhead. Do not convert the result into a universal core recommendation.
- Calculate storage. Add model shards, source data, indexes, caches, containers, logs and temporary space at their expected peaks. Include room for upgrades and rebuilds.
- Check compatibility. Confirm that the operating system, drivers, accelerator runtime, serving engine, model format and container images support the planned combination. A capacity calculation cannot compensate for an unsupported software stack.
- Run a representative workload. Use the intended model and software versions, exercise the expected concurrency and context lengths, and include retrieval and tool calls if they will be present in production.
- Set operating limits. Document maximum context, concurrency, request rate, cache size and disk-retention policies. These limits make the sizing assumptions enforceable.
- Re-size after observation. Compare measured peaks with the budget and adjust placement, limits or hardware before opening the system to more users.
What to measure during validation
- Memory: peak host RAM, accelerator VRAM, KV-cache occupancy, allocator failures and whether any worker is killed.
- CPU: per-process utilization, run-queue pressure, API-server saturation and time spent in preprocessing or retrieval.
- Latency and throughput: queue time, time to first token, generation rate and tail latency at the planned concurrency.
- Storage: free capacity, growth rate, model-load time, index-read behavior, temporary-file peaks and log-retention impact.
- Reliability: out-of-memory events, worker restarts, request timeouts, failed tool calls and behavior during model reloads.
These measurements are deployment-specific. The official sources do not provide one benchmark procedure or target utilization that applies to every agent, so report what your own run measured rather than implying a universal test result.
Diagnose common sizing failures
| Symptom | Likely sizing issue | Checks and corrective actions |
|---|---|---|
| Model loads, then fails when users connect | KV cache or concurrent-request demand exceeds available RAM or VRAM | Check cache occupancy and peak concurrency; reduce context or concurrency, use a smaller model or add usable memory. |
| A worker is killed on a multi-socket CPU host | Its NUMA node cannot hold the worker’s weights and cache | Inspect NUMA placement and worker affinity; rebalance placement or use a topology that fits each worker. |
| GPU memory is full while CPU and disk are idle | Accelerator capacity, not general system capacity, is limiting the configuration | Lower cache or concurrency, change model format, use additional supported accelerators or move inference to a suitable host. |
| Requests queue despite apparently spare CPU | API-server, batching, affinity or another serialized stage is the bottleneck | Measure each process and queue; test the actual serving configuration rather than adding cores blindly. |
| Upgrades or index rebuilds fail for lack of disk | Temporary, image or duplicate-data space was omitted | Measure peak ephemeral use, rotate logs, remove stale images and reserve explicit upgrade headroom. |
| The agent is responsive but generation is too slow | Model and hardware do not meet the latency target, or CPU preprocessing is saturated | Measure time to first token, generation rate and preprocessing separately; revisit model size, accelerator choice, concurrency and topology. |
How much RAM, CPU and storage do common plans need?
The honest answer depends on the model and service limits. A small agent that calls a remote inference endpoint has a very different profile from an agent that keeps a large model, an embedding model and a vector index on one host. The supplied official material does not establish universal minimums for either case.
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- If inference is remote, size the local machine for the agent, retrieval, tools, data and operating system; document the remote service’s limits separately.
- If inference is local on CPU, start with the model’s weight shards and configured KV cache, then verify NUMA fit and add memory for every co-resident service.
- If inference is local on a GPU, size usable VRAM for weights, cache and concurrency first; host RAM and CPU still need to support loading, preprocessing and the agent stack.
- If several models remain loaded, sum their resident footprints rather than sizing only the largest one.
- If a distributed manifest is your reference, reproduce its topology and software assumptions before using its numbers. Otherwise treat its allocations as scale illustrations only.
Keep the sizing document current
Record the model revision, precision, serving-engine version, cache and concurrency limits, host topology, measured peaks and date of validation. Versioned serving guidance can change, and a model update or longer context window can alter memory requirements without changing the agent code. Re-run the representative workload after those changes and after adding a new resident model, index or retention policy.
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