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For local inference, GPU memory sets the initial limit. NVIDIA reports that Qwen 3.6 35B runs on roughly 20 GB of memory, while 120-billion-parameter models require at least 70 GB; both figures are planning anchors whose actual requirements change with quantization, context length, runtime overhead, and concurrent agents. Cloud deployments replace the need to own that GPU, but they still require persistent data stores, isolated execution, identity controls, and a way to evaluate and roll back changes.
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What a self-evolving agent actually needs
A self-evolving agent improves prompts, tools, memory, or workflows from interaction data and feedback. That improvement must happen inside an engineering loop rather than through unrestricted self-modification. NVIDIA describes four foundational building blocks:
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- Large language model: the reasoning and generation engine, hosted locally or through an API.
- Agent harness: the orchestration layer that manages plans, tool calls, retries, state transitions, and hand-offs between agents.
- Secure runtime: an isolated environment for generated code, browser automation, files, and network access.
- Memory modules: short-term context plus durable knowledge, user facts, embeddings, and workflow history.
Surrounding those blocks is a control plane. It stores versions of prompts and tools, records traces, runs evaluations, enforces permissions, and provides approval and rollback when a proposed change affects capabilities or data access.
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Local hardware sizing
Start with VRAM, not the CPU
Model weights, the active context window, key-value cache, runtime libraries, and other processes all consume memory. Concurrent agent loops multiply that demand. A model that fits for a single short request can fail when several browser sessions, long documents, or parallel tool calls are active.
| Workload reference | Published memory guidance | How to use the figure |
|---|---|---|
| Qwen 3.6 35B | Roughly 20 GB of memory (NVIDIA, 2026) | Planning anchor for one quantized local model; leave headroom for context, runtime overhead, and tools. |
| 120-billion-parameter model | 70 GB or more of memory (NVIDIA, 2026) | Usually calls for a high-memory accelerator or a multi-device/offload design. |
| NVIDIA DGX Spark | 128 GB unified memory and 1 petaflop of AI performance (NVIDIA, 2026) | Reference point for sustained local agent workloads, not a guarantee of a particular model’s throughput. |
Quantization can reduce weight memory, while longer context windows and larger batches increase it. CPU offload lets a model start on a smaller GPU, but usually increases latency and competes with browser, retrieval, and code-execution processes. Size for the largest context and concurrency you will actually permit, not only for the parameter count on a model card.
System RAM and CPU
System memory holds the operating system, container layers, vector indexes, document parsers, browser processes, model offload, and temporary artifacts. CPU cores are consumed by tokenization, retrieval, embedding generation, network services, and sandboxed tools. There is no universal CPU or RAM minimum for an agent because these loads depend on the model, tool mix, and number of loops.
For a cluster control plane, NVIDIA Run:ai’s current v3.0 documentation lists a minimum of 10 CPU cores, 12 GB of memory, and 110 GB of disk. Its cluster system nodes require 10 cores, 20 GB of memory, and 50 GB of disk; worker nodes require 2 cores and 4 GB of memory, plus a supported NVIDIA GPU and shared storage. Those are Run:ai platform requirements, not a sizing rule for every agent application.
Storage and host requirements
- NVMe storage: keep model weights, container layers, tokenizer files, caches, transcripts, checkpoints, and evaluation sets on fast local storage. Capacity should be calculated from the models and retention policy you choose.
- Linux-capable host and containers: use a container runtime so the model server, orchestrator, tools, and monitoring components can be upgraded independently.
- Sandbox: execute generated code and browser automation in an isolated container or micro-VM with restricted files, processes, credentials, and network destinations.
- Power and cooling: long-running inference and parallel agents create sustained load. The cited platform guidance does not establish a universal wattage, thermal, or disk minimum, so validate the complete system under your intended duty cycle.
- Reliable connectivity: agents often call hosted models, search systems, SaaS APIs, registries, and telemetry endpoints even when the main model is local.
Cloud deployment patterns
Cloud resources should match the agent’s lifecycle instead of forcing every workload into a permanently running server. Google Cloud Run identifies four useful patterns:
| Cloud Run resource | Best fit | State and scaling behavior |
|---|---|---|
| Services | Stateless, request-driven agents with variable traffic | Autoscaling and scale-to-zero when idle; keep durable state outside the service. |
| Instances | Dedicated, stateful, always-on singleton loops | Predictable process identity for a long-lived coordinator or supervisor. |
| Worker pools | Distributed background fleets consuming Kafka or Pub/Sub tasks | Parallel task processing without tying work to an interactive request. |
| Jobs | Run-to-completion work such as batch evaluation, ingestion, or scheduled synchronization | Finite executions with explicit completion and retry behavior. |
Cloud Run can connect an agent runtime to Gemini or Vertex AI endpoints, GPU-enabled services for custom models, Redis for short-term memory, Firestore or Vertex AI Agent Engine Memory Bank for long-term memory, and PostgreSQL with pgvector or AlloyDB for vector search. MCP tools, ordinary API calls, browser and operating-system automation, and sandboxed code execution can be attached as separate, permissioned capabilities.
AWS guidance follows a similar separation of concerns. Lambda suits lightweight logic; ECS or Fargate suits stateful or resource-intensive containers; EventBridge carries multi-agent messages; Step Functions records checkpoints and recovery paths; DynamoDB stores shared state; S3 retains evaluation data and audit logs; and CloudWatch supplies monitoring. Managed identity and memory services reduce the amount of security-sensitive infrastructure your team must implement itself.
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Design memory as three different data systems
Putting every transcript into one vector database creates poor retrieval, excessive cost, and weak auditability. Separate data by purpose:
| Data class | Contents | Typical latency and retention need |
|---|---|---|
| Working memory | Current conversation, scratch variables, tool results, and active plan | Low latency; expires with a session or workflow unless explicitly promoted. |
| Long-term memory and grounding | Distilled user or task facts, documents, embeddings, and structured knowledge for retrieval-augmented generation | Persistent; indexed for retrieval and governed by correction and deletion rules. |
| Operational evidence | Raw transcripts, tool inputs and outputs, workflow state, evaluations, and audit records | Append-oriented and durable; retention is set by compliance, debugging, and cost requirements. |
Google describes this separation as short-term context, persistent knowledge and memory, and an operational data lake. Promote information into long-term memory only after deciding whether it is accurate, useful, allowed to persist, and attributable to a source. Keep raw evidence even when you distill it, because operators need the original trace to diagnose a bad memory or an unsafe decision.
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Security and networking are part of the runtime
An agent that can write code, browse, send messages, or modify records is an automation service with production privileges. Use private networking where possible, TLS for service-to-service traffic, workload identity instead of embedded keys, centralized secret management, and explicit egress controls. Give each tool the smallest set of permissions it needs and issue short-lived credentials for high-risk operations.
For multi-agent systems, authenticate agent-to-agent messages and isolate each agent’s state. A planner should not automatically inherit the credentials of a payment, deployment, or database-writing tool. Route sensitive actions through an approval gate, and record the identity, prompt version, tool arguments, result, and approval decision.
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Build a controlled self-evolution loop
Self-improvement becomes manageable when every change is treated like a software release.
- Collect high-fidelity traces. Record model decisions, retrieved documents, tool calls, arguments, outputs, latency, errors, user feedback, and the exact versions of prompts and models involved.
- Identify a bounded improvement. Examples include changing a retrieval filter, adding a validation step, revising a prompt, or introducing a narrowly scoped tool. Define the expected benefit and the permissions the change would require.
- Propose the change in a sandbox. Keep candidate prompts, policies, code, and tool definitions separate from the production version. Prevent the candidate from granting itself broader credentials or network access.
- Run regression and safety evaluations. Test representative tasks, known failure cases, refusal behavior, data-leakage scenarios, tool authorization, and recovery from timeouts. Compare against the current version rather than relying on a single success score.
- Require approval for capability changes. A wording tweak may be automatically promoted; a new tool, data source, permission, or external side effect should require a human or an explicit policy gate.
- Release with versioning and rollback. Store immutable versions of the model configuration, prompts, tools, memory schemas, and evaluation results. Make the previous known-good version selectable without rebuilding the entire system.
- Monitor after promotion. Watch task-completion metrics, error rates, latency, tool-denial events, user feedback, cost signals, and safety alerts. Automatically pause a version when thresholds are exceeded.
Google calls for execution traces, task-completion metrics, and user feedback; AWS guidance adds evaluations, guardrails, monitoring, and audit trails. Together they define the minimum evidence needed to know whether an agent is improving or merely behaving differently.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose local, cloud, or a hybrid design
| Decision axis | Local GPU workstation | Cloud runtime | Hybrid approach |
|---|---|---|---|
| Model execution | Fixed accelerator capacity and direct hardware control | Hosted endpoints or rented GPU services with elastic capacity | Keep a small local model for routine work and burst larger jobs to the cloud. |
| Latency and throughput | Consistent when demand fits the machine; concurrency is bounded by VRAM and thermals | Scale out or select larger instances, subject to regional capacity and service quotas | Route latency-sensitive tasks locally and overflow workloads remotely. |
| Data residency | Maximum physical control if all tools and stores remain on-premises | Depends on region, provider controls, and where logs and backups are stored | Keep sensitive memory and audit data private while sending only permitted tasks to hosted models. |
| Operational burden | You maintain GPUs, drivers, cooling, containers, patching, and spare capacity | Provider manages much of the infrastructure, but you configure identity, networking, quotas, and observability | Operate only the components that need local control. |
| Cost shape | Up-front hardware and predictable utilization cost | Usage-based and potentially elastic; total spend depends on model calls, storage, egress, and idle resources | Use local capacity for a baseline and cloud resources for peaks or experiments. |
No universal monthly bill or bandwidth requirement can be stated. Capacity and cost vary with model, quantization, context length, concurrent loops, tool-call frequency, retention, region, and availability.
Practical reference architectures
Single developer or laboratory
Use one Linux workstation with a GPU sized to the chosen model, ample system RAM, NVMe storage, a container runtime, and a sandbox. Run the model server and orchestrator locally. Put session state in a low-latency store, keep durable memories and documents in a database with vector search, and archive traces to separate storage. Start with one agent loop and add concurrency only after measuring memory use and tool isolation.
Team serving variable traffic
Deploy the stateless request path as a Cloud Run service or equivalent container service. Put long-running supervisors in instances, background tasks in worker pools, and evaluations or ingestion in jobs. Keep Redis or an equivalent store for working context, a durable database for memory and vector retrieval, and object storage for traces and evaluation sets. Connect to a hosted model endpoint or GPU service and enforce per-tool identity at the service boundary.
Regulated or high-risk automation
Separate private data stores from the model-execution network, use private endpoints and strict egress rules, and require approval for every capability or permission change. Store immutable audit records, evaluation results, and rollback artifacts. Use a state-machine workflow with checkpoints so a failed or revoked agent can resume safely without repeating an external side effect.
Common failure modes and fixes
- Model loads but crashes during long tasks: reduce context or concurrency, increase VRAM headroom, or move the key-value cache and selected layers to a larger device.
- Retrieval returns irrelevant memories: separate working memory from durable knowledge, improve chunking and metadata filters, and keep structured facts outside the vector index when exact matching matters.
- Agents lose state after a restart: persist workflow checkpoints and tool results outside the process; do not treat container memory as durable storage.
- A new version performs well in demos but regresses in production: replay representative traces, include safety and denial cases in the evaluation set, and gate promotion on comparison with the previous version.
- Tool use creates an unacceptable blast radius: isolate the tool, narrow its identity, restrict egress, require approval for side effects, and log every argument and result.
- Cloud spending becomes unpredictable: set concurrency and retention limits, scale stateless services to zero when appropriate, schedule batch jobs, and monitor model calls, storage, and egress separately.
Infrastructure checklist
- Model memory fits the largest context and permitted concurrency.
- CPU, system RAM, and NVMe capacity cover retrieval, browsers, sandboxes, caches, traces, and checkpoints.
- Generated code and browser automation run in isolated sandboxes.
- Working memory, long-term memory, and operational evidence use separate retention and access policies.
- Prompts, tools, workflows, memory schemas, and model settings are versioned.
- Regression, safety, and permission evaluations run before promotion.
- Identity, secrets, TLS, private networking, egress controls, and per-tool permissions are enforced.
- Traces, user feedback, task metrics, guardrail events, and audit records are retained long enough to investigate failures.
- Rollback and workflow recovery have been exercised, not merely documented.
The practical rule is simple: size the accelerator for the model and concurrency, then build the durable control plane that makes improvement safe. A powerful GPU without persistent memory, isolated tools, evaluations, and rollback is only a fast model server, not a dependable self-evolving agent.
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