Building the Right PC for Local AI
Tech GuruThe short answer: choose the model, quantization, context length, and number of simultaneous users first; then buy enough usable accelerator memory for that workload with headroom for the key-value cache and runtime. For a new general-purpose homelab build, 16GB of VRAM and 64GB of system RAM is a practical comfort target, while 24GB or more is the better starting point for larger coding and agent models.
For most home inference workloads, accelerator memory matters more than a small difference between desktop CPUs. VRAM is not the whole performance story, however: runtime support, memory bandwidth, prompt processing, context length, power, and whether the workload spills into system RAM can change the result.
This guide is for beginners. You do not need to know how neural networks work. The goal is to help you buy or build a PC that runs local chat models, image generators, transcription tools, coding helpers, and maybe a media server without wasting money on the wrong parts.
The Simple Version
Interactive PC Build Flow: Where Your Money Matters
Click each step to see what it means in a beginner-friendly local AI setup.
Choose a box above to view details.
If you only remember one thing, remember this:
> Buy as much GPU VRAM as you can reasonably afford, then build the rest of the PC around it.
The CPU, RAM, storage, power supply, and cooling still matter. They just do not usually matter as much as the GPU for local AI.
| If you are building for... | Start here |
|---|---|
| Trying local AI for the first time | 8GB to 12GB VRAM, 32GB system RAM, 1TB NVMe SSD |
| A comfortable everyday local AI box | 16GB VRAM, 64GB system RAM, 2TB NVMe SSD |
| Larger chat models and image generation | 24GB to 32GB VRAM, 64GB to 128GB system RAM, 2TB to 4TB NVMe SSD |
| Serious experiments, fine-tuning, or many users | 48GB+ VRAM, 128GB+ system RAM, workstation-class power and cooling |
| Media server plus AI on one machine | Intel CPU with Quick Sync or a separate small transcoding GPU, plus enough headroom for the AI GPU |
What "Local AI" Needs from a PC
Local AI means the model runs on hardware you own instead of a cloud server. That can include:
- Chat models through tools like Ollama, LM Studio, llama.cpp, text-generation-webui, or Open WebUI.
- Image generation through Stable Diffusion, Flux-style workflows, ComfyUI, or similar tools.
- Speech-to-text through Whisper-style models.
- Text-to-speech voice models.
- Coding assistants that run on your LAN.
- Retrieval-augmented generation, where your AI searches your documents and answers from them.
These jobs stress different parts of the computer.
| Component | What it does in local AI | Beginner translation |
|---|---|---|
| GPU | Runs most of the AI math quickly | The engine |
| VRAM | Holds the model while the GPU works | The engine's workbench |
| System RAM | Holds the operating system, apps, model spillover, and file caches | The garage floor |
| CPU | Handles the OS, app logic, tokenization, downloads, and fallback work | The shop manager |
| Storage | Holds models, datasets, containers, logs, media, and snapshots | The shelves |
| PSU | Feeds stable power to the GPU and everything else | The electrical service |
| Cooling | Removes heat so parts stay fast and reliable | The ventilation |
VRAM: The Most Important Spec
VRAM is the memory built into the graphics card. It is not the same as regular system RAM.
A PC with 64GB of RAM and a GPU with 12GB of VRAM does not have a 76GB GPU. The GPU still has 12GB of fast memory. Some tools can spill work into system RAM, but that is usually much slower.
Think of an AI model like a large set of books. If the whole set fits on the desk, you can work quickly. If half the books are in another room, you spend a lot of time walking back and forth. VRAM is the desk.
Why Model Size Is Confusing
You will see models described as 7B, 8B, 13B, 14B, 30B, 32B, 70B, and so on. The "B" means billion parameters. More parameters usually means the model can know and reason more, but it also needs more memory.
Then you will see labels like Q4, Q5, Q8, FP16, GGUF, AWQ, GPTQ, and bitsandbytes. These are different ways to store or compress the model.
For beginners:
- FP16 is high quality but uses a lot of memory.
- Q8 is smaller with light compression.
- Q4/Q5 are common home-user choices because they shrink models enough to fit consumer GPUs.
- GGUF is a common file format used by llama.cpp and many local model tools.
- Quantization means storing model weights with fewer bits so the model uses less memory.
Quantization is the reason a model that would be too large in full precision can still run on a home GPU.
VRAM Tiers: What You Can Actually Do
These tiers are practical starting points, not hard laws. Exact results depend on model architecture, quantization, context length, batch size, driver support, and the tool you use.
| VRAM tier | What it is good for | Common examples | Beginner verdict |
|---|---|---|---|
| 8GB | Small chat models, small coding models, Whisper, learning the tools, light image generation | RTX 4060 8GB, RTX 5060 8GB, older RTX 2070/3070-class cards | Usable, but you will hit limits quickly |
| 12GB | Better starter tier for chat, 7B/8B models with room, some 13B/14B quantized models, decent image workflows | RTX 3060 12GB, RTX 4070 12GB, RTX 5070 12GB, Intel Arc B580 12GB | The practical minimum I would target for a new budget build |
| 16GB | Comfortable home tier, larger contexts, 13B/14B models, some 20B-class models, better image generation | RTX 4060 Ti 16GB, RTX 4080/4080 Super 16GB, RTX 5080 16GB, RX 9070 XT 16GB | Strong sweet spot for most beginners who want room to grow |
| 24GB | Large-model hobbyist tier, better 30B/32B-class options, heavier image workflows, more multitasking | RTX 3090 24GB, RTX 4090 24GB, RTX 3090 Ti 24GB | Excellent used-GPU target if power and cooling are handled |
| 32GB | High-end consumer tier, bigger local models with less compromise, more context, stronger all-in-one box | RTX 5090 32GB | Great if the budget allows it, but do not starve the rest of the build |
| 48GB+ | Workstation tier, very large models, heavier fine-tuning, multi-user serving, professional workloads | RTX 6000 Ada 48GB, RTX PRO 6000 Blackwell 96GB, multiple 24GB GPUs | Expensive and often overkill for a first homelab |
A Beginner-Friendly VRAM Rule
For chat models, look at the model file size and leave extra room.
If a model download is 10GB, do not assume an 8GB GPU will be comfortable. You need memory for the model plus the key-value cache, runtime buffers, display use, and any vision or concurrent workload. There is no reliable fixed percentage that covers every architecture and runtime.
Use the model card and runtime's memory estimate as a first screen, then load the exact quantization at the intended context length and measure peak allocation. Leave additional headroom rather than planning around a one-time barely-fits result.
If the model barely fits, it may run, but it can slow down, crash, or force you to lower the context length.
CPU: Important, but Not the Star
For local AI, the CPU matters most when:
- You run models partly or entirely on the CPU.
- You host many services at the same time.
- You use the same box as a media server, NAS, game server, or development machine.
- You do data prep, embeddings, file indexing, or compression.
- You run virtual machines or many Docker containers.
For a normal local AI box, you do not need to chase the most expensive CPU. A modern 6-core or 8-core CPU is fine for many builds. More cores help if the machine is also a server.
| CPU class | Good for | Notes |
|---|---|---|
| 6 cores / 12 threads | Budget AI starter box | Fine if the GPU is doing the heavy lifting |
| 8 cores / 16 threads | Best general home choice | Comfortable for Docker, Open WebUI, media tools, and background services |
| 12 to 16 cores | Heavy multitasking | Useful for VMs, NAS duties, compiling, indexing, and CPU fallback |
| Workstation CPUs | Multi-GPU and professional workloads | Consider only if you need PCIe lanes, lots of RAM, or many expansion cards |
Intel vs AMD for a Local AI Homelab
Both work.
Intel can be attractive if you also run Plex or Jellyfin because many Intel CPUs include Quick Sync video hardware. Quick Sync can handle media transcoding without tying up your main AI GPU.
AMD is also a strong choice, especially if you want many cores, good platform value, or a gaming-plus-AI machine. If you choose AMD and also need media transcoding, you may want a small Intel Arc card, an NVIDIA card, or a separate media box.
System RAM: The Safety Net
System RAM is the memory on the motherboard. It is slower than VRAM, but you still need enough of it.
Local AI tools, Docker containers, web interfaces, vector databases, browsers, and the operating system can eat memory quickly. If a model does not fit fully in VRAM, some tools can use system RAM, but performance will be slower.
| System RAM | Who it fits | Recommendation |
|---|---|---|
| 16GB | Very light experiments only | Avoid for a new AI build unless the budget is extremely tight |
| 32GB | Beginner starter build | Good minimum for 8GB to 12GB VRAM systems |
| 64GB | Comfortable home AI box | Best default for 16GB to 24GB VRAM systems |
| 128GB | Large models, many containers, VMs, NAS plus AI | Worth it if you plan to grow |
| 256GB+ | Workstation/server territory | Mostly for multi-GPU, big datasets, or serious fine-tuning |
If you are choosing between a slightly faster CPU and more RAM, most homelab users should pick more RAM.
GPU: NVIDIA, AMD, or Intel?
The GPU market changes fast, but the software reality is simple:
- NVIDIA usually has the broadest support for local AI because many tools target CUDA first.
- AMD can work well in supported stacks, especially on Linux, but some AI tools need extra setup or have narrower support.
- Intel Arc can be a good value in budget builds and media-heavy systems, but support varies by tool.
For a beginner who wants the fewest surprises, NVIDIA is still the safest default. That does not mean AMD or Intel are bad. It means you should check your exact AI tool before buying.
| GPU vendor | Strengths | Beginner caution |
|---|---|---|
| NVIDIA | Best local AI compatibility, CUDA support, strong media encoders, lots of tutorials | Often costs more per GB of VRAM |
| AMD | Good gaming value, 16GB cards can be attractive, open-source Linux stack is improving | Some AI apps are NVIDIA-first |
| Intel Arc | Good budget VRAM options, strong media features, AV1 support | AI software support is improving but not universal |
Storage: Do Not Skimp on the SSD
AI models are big. A few downloads can fill a small drive.
Typical local AI storage users include:
- Model files.
- Quantized variants of the same model.
- Image generation checkpoints.
- LoRA files.
- Docker images.
- Python environments.
- Logs and outputs.
- Media files, if this machine is also a media server.
| Storage setup | Best for | Notes |
|---|---|---|
| 1TB NVMe SSD | Bare minimum starter | Works, but you will manage space often |
| 2TB NVMe SSD | Best beginner default | Enough for several models and tools |
| 4TB NVMe SSD | Comfortable homelab | Better for image generation and many model variants |
| SSD plus HDD array | AI plus media server | Keep apps and models on SSD; keep movies/backups on HDDs |
Use an NVMe SSD for the operating system and AI models. Hard drives are fine for bulk media storage, but they are not ideal for loading models or running databases.
Power Supply: Buy for the GPU You Actually Have
The PSU is not exciting, but it matters. Local AI can keep a GPU under heavy load for long stretches. A cheap or undersized power supply can cause crashes, shutdowns, coil noise, or cable heat problems.
Do three things:
- Check the GPU maker's recommended PSU wattage.
- Leave headroom for the CPU, drives, fans, USB devices, and future upgrades.
- Use the correct power cables. Avoid mystery adapters and overloaded splitters.
| GPU class | Sensible PSU range | Notes |
|---|---|---|
| Low-power 8GB cards | 550W to 650W | Check the exact card |
| 12GB to 16GB midrange cards | 650W to 850W | 750W is a common comfortable target |
| 24GB high-end used cards | 850W to 1000W | RTX 3090-class cards can have power spikes |
| 32GB high-end cards | 1000W+ | Follow the card vendor's recommendation |
| Multi-GPU or workstation cards | Case-by-case | Plan power, cables, outlets, and heat together |
Do not size a power supply from the CPU alone. In an AI box, the GPU is usually the largest power draw.
Cooling: Local AI Is a Long-Load Workload
Gaming often has changing load. Local AI can run the GPU hard for minutes or hours. That means cooling and airflow matter.
Beginner cooling checklist:
- Use a case with a mesh front or strong airflow path.
- Have at least two intake fans and one rear exhaust fan.
- Make sure the GPU has room to breathe.
- Avoid stuffing a hot 3-slot GPU against a solid side panel.
- Keep dust filters clean.
- Watch temperatures during real AI workloads, not just at idle.
For big GPUs, case fit is not just length. Check:
- Card length.
- Card thickness.
- Power cable clearance.
- Motherboard slot placement.
- Whether the card blocks other PCIe slots.
If the PC sounds like a jet every time you run a model, you may need a better case fan layout, a fan curve adjustment, undervolting, or a larger case.
Used GPUs: The Best Value and the Biggest Trap
Used GPUs are attractive because AI cares so much about VRAM. A used RTX 3090 with 24GB can be more useful for local AI than a newer card with less VRAM, depending on the workload.
That said, used GPUs need care.
| Used GPU check | Why it matters |
|---|---|
| Ask for photos of the exact card | Avoid bait-and-switch listings |
| Confirm VRAM amount | Similar names can have different memory sizes |
| Check power connectors | Make sure your PSU can support it safely |
| Look for physical damage | Bent fins, corrosion, missing screws, or damaged ports are bad signs |
| Ask if it was mined on | Mining is not automatic failure, but it raises cooling and wear questions |
| Test under load | Use a benchmark or AI workload and watch for crashes/artifacts |
| Check temperatures | Hot memory can shorten life |
| Prefer buyer protection | Avoid no-return deals from unknown sellers |
Good Used-GPU Targets
These are examples, not guaranteed deals:
- RTX 3060 12GB: cheap entry point with useful VRAM.
- RTX 3090 24GB: still a local AI favorite because 24GB matters.
- RTX 4090 24GB: faster and more efficient than a 3090, but usually much more expensive.
- Workstation cards with 24GB to 48GB: sometimes useful, but verify driver support, cooling style, power needs, and price.
Be careful with blower-style workstation cards. They can be useful in server cases, but they can be loud in a normal room.
When to Build a Second Box
At first, it is tempting to make one monster machine do everything: AI, Plex, Jellyfin, NAS, backups, game servers, Home Assistant, cameras, and downloads.
That can work. It can also become annoying.
Build a second box when:
- Your AI jobs slow down media streaming.
- Your media transcodes steal GPU memory from AI.
- You want the AI machine off sometimes but the media server on 24/7.
- Your storage drives make the AI workstation noisy.
- You need to reboot or break the AI stack without taking down the house services.
- Your main GPU is too valuable or power-hungry to waste on simple transcoding.
A Good Two-Box Layout
| Box | Job | Hardware personality |
|---|---|---|
| AI workstation | Runs models, image generation, experiments, coding tools | Big GPU, lots of VRAM, fast SSD, strong PSU, good cooling |
| Home server | Plex/Jellyfin, NAS, backups, Home Assistant, downloads | Efficient CPU, Quick Sync or small transcoding GPU, lots of storage, low idle power |
This split is often better than one huge always-on PC. The AI machine can sleep when you are not using it. The server can stay quiet, stable, and boring.
Media Server Coexistence
If one PC must run both local AI and Plex or Jellyfin, plan it carefully.
Media servers often need transcoding, which means converting a video into a format or bitrate that a client device can play. Transcoding can use the CPU, an integrated GPU, or a dedicated GPU.
The problem: AI also wants the GPU.
If Plex uses your AI GPU for transcoding while an LLM is loaded, you can run out of VRAM or hurt performance.
Best Coexistence Options
| Setup | Why it works |
|---|---|
| Intel CPU with Quick Sync for media, NVIDIA GPU for AI | Keeps transcoding off the main AI GPU |
| Small Intel Arc card for media, bigger NVIDIA card for AI | Useful for AMD CPU builds that still need strong media features |
| Separate media server | Cleanest long-term option |
| Direct Play media whenever possible | Avoids transcoding entirely |
| Scheduled AI jobs | Prevents heavy AI work during family movie time |
Media Server Beginner Tips
- Use clients that can Direct Play your files whenever possible.
- Store media on hard drives, but keep Plex/Jellyfin metadata and transcode cache on SSD.
- If you use Docker, pass the right GPU device into the container.
- Do not assume "has a GPU" means "Plex/Jellyfin can use it." Check the app, OS, driver, and container setup.
- Keep some VRAM free if the AI GPU is also doing media work.
Example Builds
These are templates. Prices and exact parts change too often to lock down in an evergreen article.
Budget Starter AI PC
| Part | Target |
|---|---|
| GPU | 12GB VRAM if possible; 8GB only if budget is tight |
| CPU | Modern 6-core CPU |
| RAM | 32GB |
| Storage | 1TB to 2TB NVMe SSD |
| PSU | 600W to 750W quality unit, depending on GPU |
| Cooling | Airflow case, 3 or more case fans |
| Best for | Learning, small chat models, transcription, light image generation |
Beginner note: if you are choosing between an 8GB GPU and a 12GB GPU at similar prices, take the 12GB GPU for local AI.
Comfortable Home AI Box
| Part | Target |
|---|---|
| GPU | 16GB VRAM |
| CPU | Modern 8-core CPU |
| RAM | 64GB |
| Storage | 2TB NVMe SSD |
| PSU | 750W to 850W quality unit |
| Cooling | Mesh case, strong front intake, quiet fan curve |
| Best for | Daily local chat, coding models, better image generation, Open WebUI |
This is the build class I would point most first-time serious users toward.
Big Local Model Hobbyist Box
| Part | Target |
|---|---|
| GPU | 24GB to 32GB VRAM |
| CPU | 8 to 12 cores |
| RAM | 64GB to 128GB |
| Storage | 2TB to 4TB NVMe SSD |
| PSU | 850W to 1000W+ depending on GPU |
| Cooling | Large airflow case, careful GPU clearance |
| Best for | 30B/32B-class models, larger context, heavier image workflows |
This is where used RTX 3090 cards become interesting, but only if you are comfortable checking power, cooling, and card condition.
AI Plus Media Server Box
| Part | Target |
|---|---|
| GPU | NVIDIA GPU sized for AI |
| CPU | Intel CPU with Quick Sync if possible |
| RAM | 64GB minimum; 128GB if also running NAS/VMs |
| Storage | NVMe SSD for apps/models, HDDs for media |
| PSU | Sized for GPU plus drives |
| Cooling | Extra drive cooling plus GPU airflow |
| Best for | One-box homelab where space or budget prevents a second server |
This works best if media transcoding uses Quick Sync and the AI GPU is left alone.
Common Beginner Mistakes
| Mistake | Why it hurts | Better move |
|---|---|---|
| Buying a fast GPU with too little VRAM | The model may not fit | Prioritize VRAM first |
| Building with only 16GB system RAM | Docker, browser tabs, and tools eat memory | Start at 32GB, prefer 64GB |
| Using a tiny SSD | Models fill it quickly | Start with 2TB if possible |
| Ignoring PSU cables | High-end GPUs need safe power delivery | Buy a quality PSU with the right native cables |
| Putting a hot GPU in a sealed case | Heat causes noise and throttling | Use an airflow case |
| Assuming media and AI can share one GPU forever | Transcoding and AI compete for memory | Use Quick Sync, a second GPU, or a second box |
| Buying used without testing | Bad VRAM or overheating can hide at idle | Test under load immediately |
What I Would Buy First
If I were advising a beginner in 2026, I would think in this order:
- Pick the VRAM tier.
- Pick a GPU with good software support for your tools.
- Choose a PSU that safely supports that GPU.
- Choose a case that fits and cools that GPU.
- Add 64GB RAM if the budget allows it.
- Add a 2TB NVMe SSD.
- Pick a sensible CPU and motherboard.
- Decide whether media serving belongs on this box or a second one.
The GPU decision drives the rest of the build.
Quick Buying Checklist
Before buying parts, answer these:
- What is the biggest model size I realistically want to run?
- How much VRAM does my target GPU have?
- Does my preferred AI software support this GPU well?
- Is the card physically short enough and thin enough for my case?
- Does my PSU have enough wattage and the right cables?
- Do I have at least 32GB RAM, preferably 64GB?
- Do I have at least 1TB SSD space, preferably 2TB or more?
- If this is also a media server, what hardware handles transcoding?
- Am I okay with the noise and heat of this machine running under load?
- Would a second small server make life easier?
Bottom Line
For local AI, do not build a normal gaming PC and hope it works. Build around the AI workload.
Start with VRAM. Add enough system RAM. Use fast SSD storage. Buy a real power supply. Give the GPU airflow. If the machine also runs Plex or Jellyfin, keep media transcoding away from the main AI GPU whenever possible.
A balanced local AI PC does not have to be exotic. It just needs to be honest about what local AI actually uses: GPU memory, steady power, and enough cooling to run hard without drama.
Evidence and Pre-Purchase Test Method
Evidence status: this article is documentation-backed and was fact-checked on July 15, 2026. Board memory, vendor power guidance, codec support, and quantization behavior were checked against current vendor and project documentation. TechGeeks did not assemble or benchmark the example builds for this revision, so none of the tiers is an original measured performance guarantee.
- Write the workload first. Name the exact model and quantization, expected context, number of simultaneous sessions, image or audio features, and any Plex, Jellyfin, Tdarr, virtual machine, or storage workload that will share the host.
- Prove software support before buying. Check the intended runtime's current backend matrix for your GPU, operating system, driver, quantization, and multimodal format. A supported card in one framework may fall back to CPU in another.
- Benchmark a representative system. Use the same model file and runtime settings you plan to deploy. Record cold load time, time to first token, prompt-processing and generation rates, peak RAM/VRAM, CPU use, and wall power. Run several warm repetitions, not one screenshot.
- Fill the context and add concurrency. Test the longest realistic prompt and then one to four simultaneous users. Watch for key-value cache growth, queueing, system-RAM spill, out-of-memory errors, and a change in time to first token.
- Test mixed workloads. Start a real transcode or other GPU task while inference runs. Verify which engine handles each job, whether VRAM remains available, and whether either service becomes unstable.
- Burn in the complete machine. During the return window, test VRAM, system RAM, storage, sustained CPU/GPU load, network, sleep/reboot behavior, temperatures, fan noise, and power at the wall. Save logs and serial numbers.
Set acceptance criteria before the test: the target model must fit without unplanned swapping, complete the representative task, remain stable under the intended concurrency, and stay within the noise, temperature, and circuit limits of the installation. Faster token generation cannot compensate for failed prompts or an unstable host.
Failure, Recovery, and Safety Boundaries
Keep the old GPU or a working display path until the new driver and runtime pass. Record the BIOS version and settings, export service configuration, and keep a known-good driver package. If an update breaks acceleration, stop shared services, confirm the model and data volumes are intact, revert the driver or runtime one layer at a time, and retest a small model before restoring the full workload.
Power and physical installation are safety boundaries, not tuning details. Follow the GPU and power-supply vendors' connector guidance, use the required native cables, seat connectors fully, and do not exceed the branch circuit or chassis cooling design. A used accelerator should be treated as untrusted until VRAM and sustained-load tests pass. Return a card that produces memory errors, display corruption, unexplained resets, or unsafe connector temperatures.
Back up model configuration, application databases, document indexes, and media-server metadata off the machine. Model weights can usually be downloaded again; private prompts, indexes, encryption keys, and service configuration may not be recoverable. If the AI service is reachable over a network, add authentication and firewall controls separately: owning the hardware does not make an unauthenticated model API safe.
What This Hardware Guidance Does Not Prove
- VRAM capacity does not prove that your runtime supports the card, uses the expected accelerator path, or achieves a useful prompt-processing rate.
- A model fitting at short context for one user does not prove it fits at its advertised maximum context or under concurrency.
- Vendor board power and recommended power-supply figures do not predict whole-system energy cost, transient behavior, connector layout, or your local circuit capacity.
- An independent benchmark on another driver, runtime, model, precision, or CPU does not predict your tokens per second.
- Passing synthetic stress tests does not prove generated answers are correct or that a coding agent can complete your tasks safely.
Related TechGeeks Reading
- NVIDIA vs Intel GPUs for Local AI
- Plex Homelab Architecture
- UPS Buying Guide for Home Servers
- Docker Compose for Normal People
References
- NVIDIA, "GeForce RTX 50 Series Graphics Cards," especially the compare table for RTX 5090/5080/5070/5060 memory, power, and NVENC/NVDEC details: https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/
- NVIDIA, "Compare GeForce Graphics Cards," current and previous generation VRAM and PSU recommendations: https://www.nvidia.com/en-us/geforce/graphics-cards/compare/
- NVIDIA, "RTX 6000 Ada Generation," 48GB workstation GPU reference: https://www.nvidia.com/en-us/products/workstations/rtx-6000/
- NVIDIA, "RTX PRO 6000 Blackwell Workstation Edition," 96GB workstation GPU reference: https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/rtx-pro-6000/
- AMD, "Radeon RX 9070 XT," 16GB VRAM, board power, PSU recommendation, and media codec support: https://www.amd.com/en/products/graphics/desktops/radeon/9000-series/amd-radeon-rx-9070xt.html
- Intel, "Intel Arc B580 Graphics," 12GB GDDR6, 190W board power, and 600W PSU reference: https://www.intel.com/content/www/us/en/products/sku/241598/intel-arc-b580-graphics/specifications.html
- Hugging Face Transformers documentation, "Bitsandbytes," quantization and memory-footprint concepts: https://huggingface.co/docs/transformers/en/quantization/bitsandbytes
- Hugging Face Transformers documentation, "Quantization," overview of lower-precision model storage: https://huggingface.co/docs/transformers/en/main_classes/quantization
- Ollama library page for llama2:70b, basic RAM guidance for 7B, 13B, and 70B model classes: https://ollama.com/library/llama2%3A70b
- Plex Support, "Using Hardware-Accelerated Streaming," Quick Sync, NVIDIA GPU, hardware decode/encode, and Plex Pass notes: https://support.plex.tv/articles/115002178853-using-hardware-accelerated-streaming/
- Jellyfin documentation, "Hardware Acceleration," supported acceleration methods including Intel QSV, NVIDIA NVENC/NVDEC, AMD AMF, and VA-API: https://jellyfin.org/docs/general/post-install/transcoding/hardware-acceleration/
- llama.cpp, reproducible
llama-benchoptions including context depth: https://github.com/ggml-org/llama.cpp/blob/master/tools/llama-bench/README.md - Puget Systems, independent local LLM GPU test methodology: https://www.pugetsystems.com/labs/articles/exploring-gpu-performance-across-llm-sizes/
- Puget Systems, instrumented 2026 multi-GPU inference and power methodology: https://www.pugetsystems.com/labs/articles/intel-arc-pro-b70-multi-gpu-ai-inference-performance/
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