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Tools: How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide (2026)

2026-04-03 0 views admin
Tools: How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide (2026)

How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide

Why a Consumer GPU Is Enough

VRAM: More Than Just the Model File Size

Software Setup: From Zero to Running Agent

Step 1 – Install NVIDIA Drivers

Step 2 – (Optional) Docker + NVIDIA Container Toolkit

Step 3 – Install Ollama (Recommended Runtime)

Step 4 – Pull the qwen3.5:9b Model

Step 5 – Quick Test

Step 6 – Enable the API for OpenClaw Agents

Step 7 – Docker‑Based Ollama (for Reproducibility)

Low‑Cost Upgrade Path: Scale as You Go

Tuning Your Hardware for Max Efficiency

Real‑World Example: Our Factory Hardware

Your Action Plan: Validate and Upgrade Want to run powerful AI agents without the endless API bills of cloud services? The good news is you don’t need a data‑center‑grade workstation. A single modern consumer GPU is enough to host capable 9B‑parameter models like qwen3.5:9b, giving you private, low‑latency inference at a fraction of the cost. This article walks you through the exact hardware specs, VRAM needs, software installation steps, and budget‑friendly upgrade paths so you can get a local agent up and running today—no PhD required. It’s a common myth that you must buy a professional‑grade card (think RTX A6000 or multiple GPUs linked via NVLink) to run LLMs locally. In reality, for 9B‑class models the sweet spot lies in the mid‑to‑high‑end consumer segment. In our internal testing at OpenClaw’s content factory, we compared several popular cards running the qwen3.5:9b model in its Q4_K_M quantization: The takeaway? 8 GB of VRAM is the absolute minimum but leads to frequent swapping (spilling KV cache to system RAM), which hurts stability and speed. For smooth, predictable performance you want 12 GB or more, with 16 GB being the comfortable zone that lets you keep VRAM usage below ~75% to avoid slowdowns. If your budget caps at ~$500, the RTX 4060 Ti 16 GB is a solid compromise—it trades a bit of raw tensor‑core performance for ample memory, giving you ~38 tok/s, only a few percent slower than the 5070 Ti in everyday use. Many newcomers look at the raw model file (e.g., qwen3.5:9b Q4_K_M ≈ 6.6 GB) and assume an 8 GB card will suffice. What they miss is the additional memory needed during inference: Add it up and you see why 8 GB cards start swapping once you go beyond very short prompts. For a comfortable experience with 2‑4k token contexts, aim for ≥12 GB VRAM. If you plan to experiment with longer contexts or light fine‑tuning, 16 GB gives you ample headroom. A practical rule we follow in the factory: keep VRAM utilization under 75% during generation. On a 16 GB card, that means targeting ≤12 GB used per request, leaving room for longer conversations or batch processing without hitting the swap wall. Below is a battle‑tested, step‑by‑step guide that works on Ubuntu 22.04 LTS (native or WSL2). Adjust as needed for your distro. On the host (Windows side for WSL2, or directly on Linux): After reboot, nvidia-smi should show your GPU and driver version. If you prefer an isolated, reproducible environment (handy when running multiple models): Ollama provides a simple CLI, daemon, and OpenAI‑compatible API: Verify with ollama list. First run will load the model (expect 30–40 seconds); subsequent replies should come back in a few seconds. Ollama serves an OpenAI‑style REST API on http://localhost:11434 by default. In your OpenClaw configuration, set the agent’s base_url to that address. To allow other devices on your LAN to reach it (e.g., different containers on the same machine): ⚠️ Only expose this on trusted networks. If you want everything containerized: Not everyone can drop $900 on a GPU day one. Here’s a staged approach to grow your local‑agent capability without waste. Stage 0 – Experiment with CPU (Zero Extra Cost)

If your machine only has integrated graphics or an older GTX 1060 6 GB or weaker, you can still run extremely quantized models (e.g., Q2_K) on CPU. Speeds will be modest (2–3 tok/s) but enough to validate workflows, test scripts, and get comfortable with Ollama and OpenClaw interactions. Stage 1 – Entry‑Level 16 GB Card ($250–$400)Target at least 12–16 GB VRAM to avoid memory bottlenecks. Great options: At this stage you’ll see model load times drop to 30–40 seconds and stable output around 30–38 tok/s—sufficient for trend‑scanning agents, simple drafting, and scheduled jobs. Stage 2 – Mid‑Range Card ($500–$800)When you want to run multiple 9B models simultaneously or try higher quantizations (Q5_K_M, Q6_K): With a card like the 5070 Ti you can comfortably run two 9B instances (e.g., one for trend scanning, one for content drafting) or begin experimenting with 14B‑27B models at very low quantization, leaning on system RAM for overflow. Stage 3 – Enthusiast/Professional ($1000+)If you anticipate serving multiple users, needing longer contexts, or wanting multimodal capacités later: Even the right card can be bottlenecked by software or system settings. Here are proven tweaks from our factory floor: If you see constant high utilization with rising temperatures, improve case airflow or consider a modest power‑limit tweak via nvidia-smi -pl <W> to keep thermals in check. In the OpenClaw content factory we run the following setup as our primary local‑agent platform: Under this configuration we observe: Interestingly, this same rig also powers our visual‑generation workflow via a second RTX 4090, achieving true heterogeneous compute: language handled by the 9B agent, images by the dedicated GPU, all communicating over simple text endpoints. Unsure if your current PC is ready? Follow this quick self‑audit: Identify Your GPU & VRAM Confirm Driver & CUDA HealthDownload the CUDA Toolkit’s deviceQuery sample (https://developer.nvidia.com/cuda-samples) Build and run it; you should see correct core counts and memory bandwidth. Run a Baseline Ollama TestInstall Ollama (as detailed above), pull qwen3.5:9b, and time a simple prompt: Record the first‑token delay and subsequent response speed. Define Your Typical Workload Draft an Upgrade Timeline & Budget In the era of AI, hardware is the new foundational literacy. A suitably equipped graphics card does more than make models run faster—it grants you sovereignty over your compute. You’re no longer at the mercy of rate limits, sudden pricing shifts, or vague data‑usage policies. Your agent, your data, and your costs stay firmly under your control. Pick a card that fits your budget and start experimenting today; the path to a private, cost‑effective AI agent is shorter than you think. 免費下載：https://jacksonfire526.gumroad.com/l/cdliu?utm_source=devto&utm_medium=article&utm_campaign=2026-04-02-local-agent-playbook-seo1

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# Verify current driver nvidia-smi # If missing/outdated, -weight: 500;">install the latest 550‑series -weight: 600;">sudo -weight: 500;">apt -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-550 -weight: 600;">sudo reboot # Verify current driver nvidia-smi # If missing/outdated, -weight: 500;">install the latest 550‑series -weight: 600;">sudo -weight: 500;">apt -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-550 -weight: 600;">sudo reboot # Verify current driver nvidia-smi # If missing/outdated, -weight: 500;">install the latest 550‑series -weight: 600;">sudo -weight: 500;">apt -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-550 -weight: 600;">sudo reboot # Install Docker base packages -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y ca-certificates -weight: 500;">curl gnupg lsb-release -weight: 600;">sudo mkdir -p /etc/-weight: 500;">apt/keyrings -weight: 500;">curl -fsSL https://download.-weight: 500;">docker.com/linux/ubuntu/gpg | -weight: 600;">sudo gpg --dearmor -o /etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg] \ https://download.-weight: 500;">docker.com/linux/ubuntu \ $(lsb_release -cs) stable" | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/-weight: 500;">docker.list > /dev/null -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y -weight: 500;">docker-ce -weight: 500;">docker-ce-cli containerd.io \ -weight: 500;">docker-buildx-plugin -weight: 500;">docker-compose-plugin # Add NVIDIA Container Toolkit distribution=$(. /etc/os-release;echo $ID$VERSION_ID) -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/gpgkey | -weight: 600;">sudo -weight: 500;">apt-key add - -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/$distribution/nvidia--weight: 500;">docker.list | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/nvidia--weight: 500;">docker.list -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y nvidia-docker2 -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">restart -weight: 500;">docker # Test -weight: 600;">sudo -weight: 500;">docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi # Install Docker base packages -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y ca-certificates -weight: 500;">curl gnupg lsb-release -weight: 600;">sudo mkdir -p /etc/-weight: 500;">apt/keyrings -weight: 500;">curl -fsSL https://download.-weight: 500;">docker.com/linux/ubuntu/gpg | -weight: 600;">sudo gpg --dearmor -o /etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg] \ https://download.-weight: 500;">docker.com/linux/ubuntu \ $(lsb_release -cs) stable" | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/-weight: 500;">docker.list > /dev/null -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y -weight: 500;">docker-ce -weight: 500;">docker-ce-cli containerd.io \ -weight: 500;">docker-buildx-plugin -weight: 500;">docker-compose-plugin # Add NVIDIA Container Toolkit distribution=$(. /etc/os-release;echo $ID$VERSION_ID) -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/gpgkey | -weight: 600;">sudo -weight: 500;">apt-key add - -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/$distribution/nvidia--weight: 500;">docker.list | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/nvidia--weight: 500;">docker.list -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y nvidia-docker2 -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">restart -weight: 500;">docker # Test -weight: 600;">sudo -weight: 500;">docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi # Install Docker base packages -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y ca-certificates -weight: 500;">curl gnupg lsb-release -weight: 600;">sudo mkdir -p /etc/-weight: 500;">apt/keyrings -weight: 500;">curl -fsSL https://download.-weight: 500;">docker.com/linux/ubuntu/gpg | -weight: 600;">sudo gpg --dearmor -o /etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/etc/-weight: 500;">apt/keyrings/-weight: 500;">docker.gpg] \ https://download.-weight: 500;">docker.com/linux/ubuntu \ $(lsb_release -cs) stable" | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/-weight: 500;">docker.list > /dev/null -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y -weight: 500;">docker-ce -weight: 500;">docker-ce-cli containerd.io \ -weight: 500;">docker-buildx-plugin -weight: 500;">docker-compose-plugin # Add NVIDIA Container Toolkit distribution=$(. /etc/os-release;echo $ID$VERSION_ID) -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/gpgkey | -weight: 600;">sudo -weight: 500;">apt-key add - -weight: 500;">curl -s -L https://nvidia.github.io/nvidia--weight: 500;">docker/$distribution/nvidia--weight: 500;">docker.list | -weight: 600;">sudo tee /etc/-weight: 500;">apt/sources.list.d/nvidia--weight: 500;">docker.list -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">update -weight: 600;">sudo -weight: 500;">apt-get -weight: 500;">install -y nvidia-docker2 -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">restart -weight: 500;">docker # Test -weight: 600;">sudo -weight: 500;">docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi # Install -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh # Start the -weight: 500;">service (background) ollama serve & # OR -weight: 500;">enable as a systemd -weight: 500;">service -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">enable ollama --now # Install -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh # Start the -weight: 500;">service (background) ollama serve & # OR -weight: 500;">enable as a systemd -weight: 500;">service -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">enable ollama --now # Install -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh # Start the -weight: 500;">service (background) ollama serve & # OR -weight: 500;">enable as a systemd -weight: 500;">service -weight: 600;">sudo -weight: 500;">systemctl -weight: 500;">enable ollama --now ollama pull qwen3.5:9b # pulls Q4_K_M by default # To choose a specific quantization: # ollama pull qwen3.5:9b:q5_k_m ollama pull qwen3.5:9b # pulls Q4_K_M by default # To choose a specific quantization: # ollama pull qwen3.5:9b:q5_k_m ollama pull qwen3.5:9b # pulls Q4_K_M by default # To choose a specific quantization: # ollama pull qwen3.5:9b:q5_k_m ollama run qwen3.5:9b "請用一句話介紹自己" ollama run qwen3.5:9b "請用一句話介紹自己" ollama run qwen3.5:9b "請用一句話介紹自己" ollama serve --host 0.0.0.0:11434 & ollama serve --host 0.0.0.0:11434 & ollama serve --host 0.0.0.0:11434 & FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 RUN -weight: 500;">apt-get -weight: 500;">update && -weight: 500;">apt-get -weight: 500;">install -y \ ca-certificates -weight: 500;">curl \ && rm -rf /var/lib/-weight: 500;">apt/lists/* RUN -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh RUN ollama pull qwen3.5:9b EXPOSE 11434 CMD ["ollama", "serve"] FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 RUN -weight: 500;">apt-get -weight: 500;">update && -weight: 500;">apt-get -weight: 500;">install -y \ ca-certificates -weight: 500;">curl \ && rm -rf /var/lib/-weight: 500;">apt/lists/* RUN -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh RUN ollama pull qwen3.5:9b EXPOSE 11434 CMD ["ollama", "serve"] FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 RUN -weight: 500;">apt-get -weight: 500;">update && -weight: 500;">apt-get -weight: 500;">install -y \ ca-certificates -weight: 500;">curl \ && rm -rf /var/lib/-weight: 500;">apt/lists/* RUN -weight: 500;">curl -fsSL https://ollama.com/-weight: 500;">install.sh | sh RUN ollama pull qwen3.5:9b EXPOSE 11434 CMD ["ollama", "serve"] -weight: 500;">docker build -t local-agent-ollama . -weight: 500;">docker run --rm --gpus all -p 11434:11434 local-agent-ollama -weight: 500;">docker build -t local-agent-ollama . -weight: 500;">docker run --rm --gpus all -p 11434:11434 local-agent-ollama -weight: 500;">docker build -t local-agent-ollama . -weight: 500;">docker run --rm --gpus all -p 11434:11434 local-agent-ollama -weight: 600;">sudo fallocate -l 16G /swapfile -weight: 600;">sudo chmod 600 /swapfile -weight: 600;">sudo mkswap /swapfile -weight: 600;">sudo swapon /swapfile echo '/swapfile none swap sw 0 0' | -weight: 600;">sudo tee -a /etc/fstab -weight: 600;">sudo fallocate -l 16G /swapfile -weight: 600;">sudo chmod 600 /swapfile -weight: 600;">sudo mkswap /swapfile -weight: 600;">sudo swapon /swapfile echo '/swapfile none swap sw 0 0' | -weight: 600;">sudo tee -a /etc/fstab -weight: 600;">sudo fallocate -l 16G /swapfile -weight: 600;">sudo chmod 600 /swapfile -weight: 600;">sudo mkswap /swapfile -weight: 600;">sudo swapon /swapfile echo '/swapfile none swap sw 0 0' | -weight: 600;">sudo tee -a /etc/fstab OLLAMA_MAX_LOADED_MODELS=1 OLLAMA_NUM_PARALLEL=1 ollama serve & OLLAMA_MAX_LOADED_MODELS=1 OLLAMA_NUM_PARALLEL=1 ollama serve & OLLAMA_MAX_LOADED_MODELS=1 OLLAMA_NUM_PARALLEL=1 ollama serve & #!/bin/bash while true; do echo "$(date) $(nvidia-smi --query-gpu=utilization.gpu,utilization.memory,temperature.gpu --format=csv,noheader,nounits)" >> ~/gpu_monitor.log sleep 30 done & #!/bin/bash while true; do echo "$(date) $(nvidia-smi --query-gpu=utilization.gpu,utilization.memory,temperature.gpu --format=csv,noheader,nounits)" >> ~/gpu_monitor.log sleep 30 done & #!/bin/bash while true; do echo "$(date) $(nvidia-smi --query-gpu=utilization.gpu,utilization.memory,temperature.gpu --format=csv,noheader,nounits)" >> ~/gpu_monitor.log sleep 30 done & time ollama run qwen3.5:9b "你好" time ollama run qwen3.5:9b "你好" time ollama run qwen3.5:9b "你好" - Model Weights – varies by quantization: Q4_K_M ~6.6 GB, Q5_K_M ~7.8 GB, FP16 ~18 GB. - KV Cache – grows linearly with sequence length. For a 9B model with 48 attention heads and hidden size 4096, a single token needs roughly 0.5 KB. A 2048‑token context ≈ 1 GB; 4096‑token ≈ 2 GB. - Workspace – activation values, temporary buffers, and framework overhead (Ollama, llama.cpp, etc.) typically consume another 1–2 GB. - Used RTX 3060 12 GB (~$200–$250) – check VRAM carefully; some 12 GB cards may still feel tight for longer contexts. - New RTX 4060 Ti 16 GB (~$400) – reliable, power‑efficient, and gives steady 30+ tok/s. - AMD RX 6800 16 GB (~$350) – viable if you confirm ROCm support; Ollama currently favors CUDA, but community builds are emerging. - RTX 4070 12 GB (~$500) - RTX 4070 Ti 12 GB (~$600) - RTX 5070 Ti 16 GB (~$900) – if budget allows, this is currently the best single‑card balance of VRAM, speed, and power draw. - Dual‑card setup (e.g., two RTX 4060 Ti 16 GB) with simple load‑balancing (vLLM + round‑robin) or an NVLink‑capable motherboard if you find a used workstation board. - External GPU enclosure (eGPU) via Thunderbolt 4 for laptop users who need portability. - Keep a small cloud‑API quota as a burst‑only fallback for those rare occasions when you need >32k context or true multimodality (image/video understanding). - Set Up a Swap File Prevent out‑of‑memory surprises by allocating swap at least equal to your VRAM. For a 16 GB card: - Limit Ollama’s Parallelism (if you’re a single user) Reduce contention by telling Ollama to keep only one model loaded and handle one request at a time: - Monitor GPU Utilization A lightweight logging script helps you spot under‑ or over‑use: - CPU: AMD Ryzen 9 9950X (16C/32T) - Motherboard: X670E Artisan series - RAM: 64 GB DDR5 6000 MHz (2×32 GB) - Storage: 2 TB NVMe PCIe 4.0 (system) + 4 TB SATA III (backup) - PSU: 1000 W 80+ Gold fully modular - Case: Mid‑tower with three 120 mm fans front/rear/top/bottom - GPU: NVIDIA RTX 5070 Ti 16 GB (Founders Edition) – driver 550.54.15, CUDA 12.4 - OS: Ubuntu 22.04 LTS (running inside WSL2 on a Windows 11 host) - Docker: 27.0.3 - Ollama: 0.5.0 - Model: qwen3.5:9b Q4_K_M - Model cold‑-weight: 500;">start load: ~39.4 seconds - Steady‑state request latency (200‑token output): ~1.8 seconds - 12‑hour stability test (one request per minute): zero crashes, no memory leaks - Daily throughput ≈ 12 million tokens, equating to roughly $180/day saved versus calling Claude Opus for the same volume. - Identify Your GPU & VRAM Windows: Win + R → dxdiag → Display tab. Linux: lspci -v | grep -i vga or just run nvidia-smi if drivers are installed. Note the card name and VRAM size. - Windows: Win + R → dxdiag → Display tab. - Linux: lspci -v | grep -i vga or just run nvidia-smi if drivers are installed. Note the card name and VRAM size. - Confirm Driver & CUDA Health Download the CUDA Toolkit’s deviceQuery sample (https://developer.nvidia.com/cuda-samples) Build and run it; you should see correct core counts and memory bandwidth. - Run a Baseline Ollama Test Install Ollama (as detailed above), pull qwen3.5:9b, and time a simple prompt: - Windows: Win + R → dxdiag → Display tab. - Linux: lspci -v | grep -i vga or just run nvidia-smi if drivers are installed. Note the card name and VRAM size. - Define Your Typical Workload Do you need to process very long documents (>16k tokens)? Is multimodal (image/audio) understanding required? How many agent calls per day do you anticipate? - Do you need to process very long documents (>16k tokens)? - Is multimodal (image/audio) understanding required? - How many agent calls per day do you anticipate? - Draft an Upgrade Timeline & Budget If VRAM < 12 GB, prioritize a 16 GB card (new or used). If funds are tight, consider a well‑reviewed used 16 GB model (e.g., RTX 3060 Ti 12 GB is risky due to insufficient VRAM; aim for a true 16 GB part). Remember to verify your power supply can handle the new card’s TDP and has the requisite PCIe power connectors. - If VRAM < 12 GB, prioritize a 16 GB card (new or used). - If funds are tight, consider a well‑reviewed used 16 GB model (e.g., RTX 3060 Ti 12 GB is risky due to insufficient VRAM; aim for a true 16 GB part). - Remember to verify your power supply can handle the new card’s TDP and has the requisite PCIe power connectors. - Do you need to process very long documents (>16k tokens)? - Is multimodal (image/audio) understanding required? - How many agent calls per day do you anticipate? - If VRAM < 12 GB, prioritize a 16 GB card (new or used). - If funds are tight, consider a well‑reviewed used 16 GB model (e.g., RTX 3060 Ti 12 GB is risky due to insufficient VRAM; aim for a true 16 GB part). - Remember to verify your power supply can handle the new card’s TDP and has the requisite PCIe power connectors.

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