I need to search for information about OpenClaw (Clawdbot) to provide accurate hosting recommendations.

Let me search for more specific information about LLM requirements for OpenClaw.

Now let me search for more specific information about OpenClaw's LLM integration options.

Based on my research, I can now provide comprehensive hosting recommendations for OpenClaw (Clawdbot). Here's what I found:

Best Hosting Setup for OpenClaw (Clawdbot)

Overview

OpenClaw is a self-hosted personal AI assistant that primarily uses cloud LLM APIs (Anthropic Claude, OpenAI) rather than running local LLMs. The hardware requirements are modest for the core application, with optional heavy requirements only if you want to run local LLMs.

Recommended Specifications

1. Cloud LLM Setup (Recommended - Most Users)

Minimum Requirements:

• CPU: 2 cores (any modern x86_64 or ARM64)
• RAM: 2-4 GB
• Storage: 20 GB SSD
• OS: Ubuntu 22.04, macOS, Windows (via WSL2)
• Network: Stable internet connection

Recommended for Production:

• CPU: 4 cores
• RAM: 8 GB
• Storage: 50-100 GB SSD (for logs, workspace files, conversation history)
• OS: Ubuntu 22.04 LTS / macOS

Use Cases:

• Personal AI assistant via messaging apps
• Browser automation
• File management and workspace operations
• Ideal for 90% of users

Hosting Options:

• VPS Providers: Hetzner (€3.49/mo), DigitalOcean ($12/mo), Linode ($5/mo)
• One-Click Deploy: Railway ($5-20/mo), Render (free tier available)
• Local: Raspberry Pi 4, old laptop, Mac Mini

2. Local LLM Setup (Advanced Users)

If you want to run local LLMs with OpenClaw, you'll need significantly more resources:

Small Models (7-8B parameters)

• GPU: NVIDIA RTX 4060 (8GB VRAM) or better
• CPU: 6-8 cores
• RAM: 16 GB system RAM
• Storage: 100 GB+ SSD
• Model Examples: Llama 3.1 8B, Qwen 3 8B, Mistral 7B
• Quantization: Q4_K_M (4-bit)
• VRAM Usage: ~5-6 GB
• Performance: 40-50 tokens/second
• Cost: $200-300 (used GPU)

Best GPUs for 7-8B Models:

• RTX 4060 (8GB) - $300
• RTX 3060 (12GB) - $200-250 used
• RTX 4060 Ti (16GB) - $500

Medium Models (13-30B parameters)

• GPU: NVIDIA RTX 4070 Ti (16GB) / RTX 4080 (16GB) / RTX 3090 (24GB)
• CPU: 8-12 cores
• RAM: 32 GB system RAM
• Storage: 200 GB+ NVMe SSD
• Model Examples: Llama 3.1 30B, Qwen 2.5 32B, Yi-34B
• Quantization: Q4_K_M to Q5_K_M
• VRAM Usage: 15-20 GB (Q4), 20-25 GB (Q5)
• Performance: 20-35 tokens/second
• Cost: $600-1,200

Best GPUs for 13-30B Models:

• RTX 3090 (24GB) - $800-1,000 used
• RTX 4080 (16GB) - $1,000
• RTX 4090 (24GB) - $1,600

Large Models (70B+ parameters)

• GPU: NVIDIA RTX 4090 (24GB) or RTX 5090 (32GB) / A6000 (48GB)
• CPU: 12-16 cores
• RAM: 64 GB system RAM
• Storage: 500 GB+ NVMe SSD
• Model Examples: Llama 3.1 70B, Qwen 2.5 72B
• Quantization: Q4_K_M minimum
• VRAM Usage: 40-50 GB (Q4)
• Performance: 10-20 tokens/second
• Cost: $1,600-4,000+

Best GPUs for 70B+ Models:

• RTX 4090 (24GB) - $1,600 - requires aggressive quantization
• RTX 5090 (32GB) - $2,000+ - better for 70B models
• RTX A6000 (48GB) - $4,000 - professional option
• H100 (80GB) - $25,000+ - enterprise only

VRAM Requirements by Model Size

*Requires aggressive context management for 70B models on 32GB

Performance Benchmarks

Cloud LLM (Recommended)

• Latency: 1-3 seconds for first token
• Throughput: Dependent on API (Claude Opus 4.5 ~50 tokens/sec)
• Cost: $5-50/month depending on usage
• Reliability: 99.9%+ uptime

Local LLM Performance

7B Model (Q4_K_M) on RTX 4060:

• First token: 50-100ms
• Generation: 42 tokens/second
• Context: 4K-8K tokens comfortable
• Use case: Light coding, Q&A

30B Model (Q4_K_M) on RTX 3090:

• First token: 100-200ms
• Generation: 25 tokens/second
• Context: 8K-16K tokens
• Use case: Coding assistants, complex tasks

70B Model (Q4_K_M) on RTX 5090:

• First token: 200-500ms
• Generation: 15 tokens/second
• Context: 8K-16K tokens (limited)
• Use case: High-quality outputs

Operating System Recommendations

Best: Ubuntu 22.04 LTS

• Most tested and supported
• Docker works flawlessly
• Easy VPS deployment
• All tools work out of box

Good: macOS

• Native app available
• Voice Wake features
• Canvas/A2UI support
• Local development friendly

Acceptable: Windows (WSL2)

• Requires WSL2 setup
• Some features limited
• Not recommended for servers

Cost Comparison

Cloud LLM Hosting

• VPS ($3-12/mo) + API costs ($10-50/mo) = $13-62/mo total
• No upfront hardware cost
• Scalable and reliable
• Access from anywhere

Local LLM Setup

• Hardware: $300-4,000 (one-time)
• Electricity: $10-30/month (24/7 operation)
• Break-even: 6-24 months vs cloud
• Benefits: Privacy, no API costs, offline operation
• Drawbacks: Maintenance, heat, noise, performance limitations

Recommended Deployment Strategy

For Most Users:

1. Start with cloud LLM on a small VPS (Hetzner 2GB/$3.5/mo or Railway)
2. Use Claude Opus 4.5 via API (best quality)
3. Scale VPS as needed for browser automation/skills

For Privacy/Cost-Conscious:

1. Used RTX 3060 12GB ($200-250)
2. Run Qwen 3 8B or Llama 3.1 8B (Q4_K_M)
3. Ubuntu 22.04 on local hardware
4. Good for 80% of tasks, fallback to API for complex work

For Power Users:

1. RTX 4090 24GB or RTX 5090 32GB
2. Run 30-70B models locally
3. Hybrid: local for most tasks, cloud for cutting-edge features
4. Best of both worlds

Key Considerations

Important Notes:

• OpenClaw is designed for cloud APIs - local LLM support may require additional configuration
• VRAM is the #1 bottleneck for local LLMs
• Quantization (Q4_K_M) reduces quality minimally but saves 75% VRAM
• Context length eats VRAM linearly (KV cache grows with conversation)
• Multi-GPU setups are complex but enable larger models

Network Requirements:

• Cloud LLM: Stable internet essential
• Local LLM: Offline operation possible

The sweet spot for most users is cloud LLM on a modest VPS, reserving local LLM deployment for those with specific privacy requirements or who want to avoid ongoing API costs.

Here’s a practical, performance-oriented hosting setup guide for running OpenClaw / Clawdbot, emphasizing both Cloud LLM (managed scalable inference) and Local LLM (your own hardware). I’ll break it down by CPU, GPU, RAM, storage, OS, and real-world benchmarks / sizing guidance for different local model sizes.

🧠 Core Considerations for LLM Hosting

Before we get into specs:

• For most OpenClaw/Clawdbot deployments you’re doing inference, not training. That means VRAM, RAM, and fast disk matter far more than raw CPU cores.

• Local LLMs are constrained by VRAM for non-quantized models, and benchmarking confirms this clearly — approximately 2 GB VRAM per 1 B parameters (FP16), or much less with 4-bit quantization.

☁️ Cloud LLM Hosting (Recommended for Production)

The easiest way to run Clawdbot at scale — no hardware hustle, elastic scaling, and higher reliability.

Typical Cloud Architecture

• LLM Provider: OpenAI (gpt-4.1/gpt-4o), Anthropic Claude, Cohere, etc.

• Inference Layer: Clawdbot backend connects via API (OpenAI-compatible).

• Autoscaling: Use Kubernetes or serverless functions to scale bot endpoints.

Cloud Instance Types (example AWS / GCP / Azure)

Why Cloud?

• Eliminates VRAM limits inherent to local boxes.

• Autoheals and scales with demand.

• You pay for utilization, not idle hardware.

Cloud is the “safe default”, especially if uptime and multi-user throughput matter.

🏠 Local LLM Hosting (Best for Privacy / Control)

If your use case is off-API, offline, or self-sustained, local hosting works well — but you need careful hardware planning.

💻 Hardware Stack Essentials

🌀 CPU (Central Processing Unit)

• Good Target (Local Inference): 6–16 cores (e.g., AMD Ryzen 9 / Intel i9)

• Pro Workstation: Threadripper / EPYC 24–64 cores

• GPUs do most of the heavy lifting, but fast CPU matters for token scheduling and pre/post-processing.

• Cloud equivalent: nC/T (Compute optimized vCPUs) paired with GPUs.

Minimum

• 8 cores / 16 threads (high IPC preferred)

Recommended

• 12–24 cores (higher concurrency + batching)

🧠 GPU (Graphics / AI Accelerator)

This is the bottleneck for local LLM inference. VRAM determines what model size you can load.

Key Notes

• Consumer cards like RTX 4090 (24 GB) or RTX 6000 Ada (48 GB) are the sweet spot for 7B–30B with quantization. +1

• Quantization (4-bit / 8-bit) drastically lowers VRAM needs — a 30B model can often be run on ~24 GB VRAM with clever offload.

• Enterprise deployments (multi-user or >30B): A100/H100 class in cloud or rack servers.

Example GPU Choices

• Entry / Budget: RTX 3060 (12 GB) — good for <13B quantized.

• Sweet Spot: RTX 4090 (24 GB) — robust for 7B, 13B quantized, and decent performance.

• Heavy Local / Enterprise: A100 80 GB / H100 80 GB — for 30B+ and high throughput.

📦 RAM (System Memory)

System RAM feeds the VRAM for offload and holds token contexts and OS overhead.

• Larger context windows or multi-model hosting → err toward high RAM.

💾 Storage

• NVMe SSD (Fast) is critical — loading large model files quickly improves startup latency and reduces pause times.

• Recommended:

  • Minimum: 500 GB NVMe

  • Recommended: 1–2 TB NVMe

  • Heavy Use / Multi-Model: 2 TB+ (with backups)

🧩 Operating System

Your OS choice should match your tooling and GPU drivers:

📏 Performance Estimates (Local LLM Inference)

These are ballpark throughput estimates based on community benchmarks — your results depend on model type, quantization, context length, and runtime (lmstudio/ollama/koboldcpp etc.).

Throughput (Tokens/sec) on a 16–24 GB GPU

These measurements come from real community tests using common LLM inference engines.

🧠 Summary: Best Hosting by Use Case

🔹 Starter / Hobby Local

• CPU: 8 cores

• GPU: RTX 3060 / 4060 (12–16 GB VRAM)

• RAM: 32 GB

• Storage: 1 TB NVMe

• Capability: 7B–13B quantized models

🔹 Power Local

• CPU: 12–24 cores

• GPU: RTX 4090 / RTX 6000 Ada (24–48 GB)

• RAM: 64 GB

• Storage: 1–2 TB NVMe

• Capability: 7B–30B quantized models; solid inference speed

🔹 Enterprise / Multi-User

• CPU: 32+ cores

• GPU: A100/H100 80 GB multi-GPU

• RAM: 128 GB+

• Storage: 2 TB+ NVMe

• Capability: 30B–70B, high throughput

🔹 Cloud-First (No hardware mess)

• Managed LLM APIs (OpenAI, Anthropic etc.)

• Autoscaling compute for spikes

• Pay-as-you-use

🤔 Final Thoughts

You’re asking not just what hardware, but what makes the whole system effective. The bottleneck is VRAM and memory bandwidth — a large GPU with enough memory substantially outperforms more general compute power, especially for models >13B. Quantization tools and runtimes (gguf, 4-bit/8-bit) dramatically lower requirements if you’re OK with mild accuracy/quality trade-offs.

Whether you go cloud or local, design for future-proofing: plenty of RAM, fast NVMe SSD, and a GPU that handles your largest model without paging — that’s where responsiveness and reliability come from.

If you want, I can tailor this further to specific local deployments (e.g., LM Studio vs Ollama + Docker + Kubernetes) or give a cost-optimized parts list for a budget build.

To run OpenClaw (formerly known as Moltbot or Clawdbot) effectively, your choice of hosting depends entirely on whether you intend to use Cloud APIs (like Claude or GPT-4o) or Local LLMs (like Llama 3 or Mistral).

Because OpenClaw is designed to be a persistent, 24/7 assistant, the most important factor is uptime.

1. Cloud-Based Hosting (Using External APIs)

This is the most popular setup. You host the OpenClaw "brain" (the Gateway and Node) on a lightweight server, and it calls Anthropic or OpenAI for the thinking.

Recommended Specs

• Best Host: Hetzner (CPX11/21) or DigitalOcean (Basic Premium Intel/AMD).

• Why: OpenClaw itself is very light. The 4GB RAM recommendation ensures that browser automation (running a headless Chromium instance to surf the web) doesn't crash the server.

2. Local LLM Hosting (Self-Hosted AI)

If you want 100% privacy or to avoid API costs, you need a machine with a dedicated GPU. OpenClaw connects to local backends via Ollama or vLLM.

Hardware Requirements by Model Size

For local hosting, VRAM (Video RAM) is the "hard" requirement. If the model doesn't fit in VRAM, it will "spill" into system RAM, making the bot significantly slower (from ~50 tokens/sec to ~2 tokens/sec).

The "Ultimate" Local OpenClaw Setup (2026)

• CPU: Intel Core i7-14700K or AMD Ryzen 9 7950X (for fast pre-processing).

• GPU: NVIDIA RTX 5090 (32GB VRAM). This is the 2026 gold standard, allowing you to run 30B+ models with large context windows (vital for OpenClaw's "memory").

• RAM: 64GB DDR5 (OpenClaw uses memory for its vector database and long-term context storage).

• Storage: 2TB NVMe Gen5 (for fast loading of multi-gigabyte model weights).

• OS: Linux (Ubuntu/Pop!_OS) is strictly superior to Windows for local LLMs due to better CUDA driver stability and Docker performance.

3. Performance Benchmarks (Estimated)

Using a 4-bit quantized (GGUF) model on a modern consumer setup:

Summary Recommendation

1. For most users: Rent a $10/month VPS (Ubuntu 24.04, 4GB RAM) and use an Anthropic API Key. This provides the best "smartness" to cost ratio without managing hardware.

2. For Privacy/Enthusiasts: Build or use a PC with an NVIDIA RTX 3090/4090/5090. Use Ubuntu and run Ollama as the backend.

3. For Mac Users: A Mac Mini M4 (16GB RAM minimum) is a fantastic, power-efficient "always-on" OpenClaw server, though it is limited to smaller 7B–14B models.

Would you like me to generate a Docker Compose file to help you deploy OpenClaw on your chosen server?

Clawdbot (also known as OpenClaw or Moltbot) is a lightweight Node.js-based personal AI assistant gateway that integrates seamlessly with local LLMs via Ollama's OpenAI-compatible API. It runs efficiently on minimal hardware but benefits from GPU acceleration for local LLM inference.github+3

Base Requirements

Clawdbot Gateway alone needs 1-2 vCPU, 2GB+ RAM, 500MB+ storage, and Ubuntu LTS (or macOS/Linux). Node.js ≥22 is required. For LLM integration, add Ollama with GPU support (NVIDIA CUDA recommended).clawd+2

Cloud LLM Setup

Use cloud providers like Anthropic (Claude Opus), OpenAI, or Tencent Cloud TI Platform for zero local hardware needs. Recommended VPS hosting: AWS EC2 t3.medium (2 vCPU, 4-8GB RAM, $10-20/month), DigitalOcean/Hetzner ($5-12/month). OS: Ubuntu 24.04 LTS. No GPU needed; latency depends on API (e.g., Claude Pro for tool-calling).dev+3

Local LLM Setup

Pair Clawdbot with Ollama for models like Llama 3.3 or Qwen2.5 (tool-capable). Enable via OLLAMA_API_KEY="ollama-local"; auto-discovers models at http://127.0.0.1:11434.clawd​YouTube​

Hardware Recommendations

LLM Size Benchmarks

VRAM/RAM estimates for Q4_K_M quantization (common for balance); tok/s assumes RTX 4090, batch=1, 2048 ctx.local-ai-zone.github+3

Gateway overhead: +1-2GB RAM. For multi-user, scale RAM/VRAM linearly. Test with ollama run <model> for exact perf.developer.nvidia+1

Cloud LLM Setup (e.g., Using Anthropic Claude, OpenAI GPT via API)

OpenClaw is lightweight (Node.js-based) and primarily designed for cloud LLMs by default, which offload inference to remote APIs. This makes it ideal for always-on, low-cost hosting with no GPU required.

• Recommended Hosting: Cheap VPS (e.g., DigitalOcean, Hostinger, Hetzner) or home server/Raspberry Pi for 24/7 availability. Local laptop/desktop works but may sleep.
• CPU: 2-4 cores (e.g., Intel/AMD/ARM modern processor).
• GPU: None required.
• RAM: 2-8 GB (4 GB sufficient for personal use; more for multiple concurrent agents).
• Storage: 20-50 GB SSD (for OS, Node.js, Docker, logs, and config).
• OS: Linux (Ubuntu 22.04/24.04 recommended for stability and Docker support). macOS or Windows (via WSL2) also works.
• Performance Notes: Response times depend on API provider (e.g., Claude Opus: 1-10 seconds per response). Low latency with good internet. Cost: API usage fees only (e.g., Anthropic Claude).
• Why Best: Reliable, private (data stays local except API calls), and cheap (~$5-20/month VPS).

Local LLM Setup (via Ollama Integration)

OpenClaw supports local LLMs through Ollama's OpenAI-compatible API endpoint. Ollama handles model inference, so resources are dominated by the model size and quantization (e.g., Q4_K_M or Q5_K_M for efficiency). CPU-only is possible but slow (1-10 tokens/second); GPU strongly recommended for usable speeds.

• Recommended Hosting: Local desktop/home server with NVIDIA GPU for best performance/privacy. Cloud GPU instances (e.g., RunPod, Vast.ai) for testing, but expensive for 24/7 (~$0.50-2/hour). Apple Silicon Macs (M-series) excellent alternative with unified memory.
• CPU: Modern 8+ cores (e.g., Ryzen 5/Intel i7 or better) for offloading if VRAM limited.
• GPU (NVIDIA Recommended, CUDA 11.8+):
  • RTX 30/40/50 series preferred.
  • AMD ROCm possible but less stable.
  • Apple M-series for macOS.
• RAM: 16-64+ GB (system RAM supplements VRAM for context/loading).
• Storage: 50-200+ GB SSD/NVMe (models: 4-40 GB each; faster storage speeds loading).
• OS: Linux (Ubuntu recommended for NVIDIA drivers/CUDA). macOS native for Apple Silicon. Windows supported but slower setup.

Estimated Resource Requirements and Performance (Quantized Models, e.g., Q4/Q5)

These are approximate for common open models (e.g., Llama 3.1, Mistral, Qwen) via Ollama. Tokens/second (t/s) on mid-high end hardware; actual varies by context length (4k-8k typical) and quantization.

• Benchmarks Sources: Based on community reports and Ollama docs (e.g., 7B on RTX 4090: 100+ t/s; 30B on 24 GB VRAM: 20-30 t/s). Real-world: Add 20-50% overhead for OpenClaw agent thinking/tools.
• Tips for Efficiency: Use quantized models (GGUF format). Longer context reduces speed. For best local performance, combine with fast storage and cooling.

OpenClaw itself adds minimal overhead (~500 MB-1 GB RAM). Start with cloud LLMs for ease, then add Ollama for privacy/offline use. Setup via Docker recommended for isolation. Always secure (e.g., Tailscale for remote access, no public ports).

Optimal Hosting Setup for OpenClaw (Clawdbot)

Overview

OpenClaw (Clawdbot) is an open-source, full-stack AI platform with components including a UI (Next.js), backend (Python/FastAPI), embedding models, and LLMs. The optimal setup depends heavily on whether you use cloud LLMs or local LLMs.

Core Infrastructure Requirements

CPU

• Minimum: 4-6 cores modern CPU (Intel i7/Ryzen 7 or better)

• Recommended: 8+ cores (Intel Xeon/AMD EPYC or Ryzen 9/Threadripper)

• Cloud Recommendation: AWS c6i.2xlarge, GCP n2-standard-8, Azure D4s v4

RAM

• Minimum: 16GB

• Recommended: 32-64GB (for local LLMs + embeddings)

• For 30B+ models: 64-128GB

• Critical: Ensure high bandwidth (3200MHz+ DDR4/DDR5)

Storage

• Type: NVMe SSD (PCIe 4.0 preferred)

• Minimum: 100GB

• Recommended: 512GB-1TB+

• Considerations: Vector databases and document storage can grow quickly

OS

• Primary: Ubuntu 22.04 LTS or 24.04 LTS

• Alternatives: Debian 12, RHEL 9 (for enterprise)

• Container: Docker + Docker Compose (recommended deployment)

Local LLM Configurations

GPU Recommendations by Model Size

Note: 13B F16 fits in 24GB with optimization

Professional/Server GPUs

• Budget Tier: Dual RTX 3090 (48GB total, ~$2,500 used)

• Mid Tier: RTX 4090 + RTX 3090 (48GB total, excellent speed)

• Pro Tier: NVIDIA A6000 (48GB VRAM) or dual A5000 (48GB total)

• Enterprise: NVIDIA A100 40/80GB or H100 (cloud-only practical)

VRAM Optimization

• Use quantization (GPTQ, AWQ, GGUF) to reduce VRAM

• GGUF models allow CPU+GPU offloading (llama.cpp)

• For 30B+ models, consider split across multiple GPUs

Cloud vs Local Decision Matrix

Cloud LLM Setup (Recommended for most users)

yaml

Infrastructure:
- CPU: 4-8 vCPUs
- RAM: 16-32GB
- Storage: 100GB NVMe
- Network: 1Gbps+
- Cost: $50-200/month

Cloud LLM Services:
- OpenAI GPT-4/GPT-4o
- Anthropic Claude 3
- Google Gemini Pro
- Azure OpenAI
- Together.ai (open models)

Pros: No GPU management, latest models, scalable
Cons: Ongoing costs, API latency, privacy concerns

Local LLM Setup (Recommended for data-sensitive applications)

Tier 1: 7B-13B Models (Entry/Mid)

yaml

Hardware:
- GPU: RTX 4060 Ti 16GB or RTX 4070 12GB
- CPU: Ryzen 7/i7 8-core
- RAM: 32GB DDR4/5
- Storage: 512GB NVMe
- OS: Ubuntu 22.04
Estimated Cost: $1,200-$1,800
Performance: 30-50 tokens/sec (7B Q4)

Tier 2: 30B Models (Advanced)

yaml

Hardware:
- GPU: RTX 4090 24GB or dual RTX 3090
- CPU: Ryzen 9/i9 12+ core
- RAM: 64GB DDR4/5
- Storage: 1TB NVMe
- PSU: 1000W+ Gold
- OS: Ubuntu 22.04
Estimated Cost: $2,500-$4,000
Performance: 15-25 tokens/sec (30B Q4)

Tier 3: 70B+ Models (Enterprise)

yaml

Hardware:
- GPU: Dual RTX 4090 or A6000/A100
- CPU: Threadripper/Xeon 16+ core
- RAM: 128GB+ ECC
- Storage: 2TB NVMe RAID
- Cooling: Proper workstation cooling
Estimated Cost: $5,000-$15,000
Performance: 5-15 tokens/sec (70B Q4)

Performance Benchmarks (Local LLM)

Embedding Model Requirements:

• BGE/Large: 1.5GB VRAM, 4GB RAM

• OpenAI embeddings: API only, no local GPU needed

Recommended Cloud Providers

For Cloud LLM (API-based)

1. VPS Providers: DigitalOcean, Linode, Vultr ($40-100/month)

2. Managed Kubernetes: AWS EKS, GCP GKE ($100-300/month)

3. Serverless: AWS Lambda + API Gateway (pay-per-use)

For Local LLM (GPU Cloud)

Deployment Configuration

Docker Compose Example

yaml

version: '3.8'
services:
openclaw-backend:
image: openclaw:latest
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
environment:
- LLM_TYPE=local # or "openai", "anthropic"
- LLM_MODEL=llama3:8b
- EMBEDDING_MODEL=bge-large
volumes:
- ./data:/app/data
ports:
- "8000:8000"

Kubernetes Resource Requests

yaml

resources:
limits:
nvidia.com/gpu: 1
memory: "32Gi"
cpu: "8"
requests:
memory: "24Gi"
cpu: "4"

Cost Analysis

Recommendations by Use Case

1. Development/Testing: Cloud LLM API + basic VPS ($60/month)

2. Small Business: RTX 4070 + 32GB RAM local setup (~$1,500 one-time)

3. Medium Enterprise: Dual RTX 4090 + 64GB RAM (~$3,500 one-time)

4. Large Enterprise: Cloud GPU instances or on-prem A100/H100 cluster

Optimal Starting Point

For most OpenClaw deployments, I recommend:

• Initial: Start with cloud LLM APIs (OpenAI/Anthropic) on a $40-80/month VPS

• Scale: Once document volume grows, deploy local 7B/13B model on RTX 4070 12GB

• Production: For 10+ users, move to RTX 4090 24GB or cloud GPU instances

Critical Considerations

1. Power Consumption: Local GPUs use 300-600W under load

2. Cooling: GPUs require proper airflow/ventilation

3. Model Updates: New models may require more VRAM

4. Parallel Users: Each concurrent user needs ~2-4GB additional VRAM

This setup balances cost, performance, and scalability for OpenClaw's mixed workload of embedding generation, RAG processing, and LLM inference.