What PC Specs Do You Need to Run an LLM Locally? (2026 Guide)
VRAM is king. Here's exactly what GPU, RAM, CPU, and storage you need to run large language models locally — without wasting money on the wrong parts.
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Last updated: April 2026
Let's cut straight to it: VRAM is the single most important spec for running LLMs locally. Everything else — CPU, system RAM, storage — plays a supporting role. Get the VRAM right, and the rest almost doesn't matter.
Here's the cheat sheet before we dive in:
- 5B models (Gemma 4 E2B) — Can run on CPU with 16 GB RAM, but an 8 GB GPU makes it usable.
- 8B models (Gemma 4 E4B) — 8 GB VRAM works, but it's tight. 16 GB is the comfortable sweet spot.
- 26B MoE models — 16 GB VRAM thanks to MoE's efficient architecture.
- 31B dense models — 24 GB VRAM minimum.
- 70B+ models — Forget consumer GPUs. You're looking at 74 GB+ VRAM, which means multi-GPU or professional cards.
Why Should You Even Bother Running AI Locally?
Models like Gemma, LLaMA, and Qwen have gotten good enough that a lot of people are ditching API subscriptions entirely. Running locally means:
- No usage limits — generate as many tokens as you want
- No monthly bill — buy the hardware once, run it forever
- Complete privacy — your prompts never touch a server
The catch? You actually have to understand the hardware. Most guides throw around jargon that makes your eyes glaze over. This one won't.
The Hardware Breakdown
GPU VRAM: The One Spec That Matters
Think of your GPU's VRAM like a desk. The model is a giant blueprint that has to fit flat on that desk to work. If the blueprint is bigger than the desk, you can't spread it out — work stops.
Here's how much VRAM different model sizes actually need (using Gemma 4 as the example, since it's Google's latest open-source release and covers a wide range of sizes):
| Model | Actual Size | VRAM at Q4 Quantization | Minimum to Run Comfortably |
|---|---|---|---|
| Gemma 4 E2B | 5B total* | ~3.5 GB | CPU + 16 GB RAM (slow), or 8 GB GPU |
| Gemma 4 E4B | 8B total* | ~5–6 GB | 8 GB (tight), 16 GB (smooth) |
| Gemma 4 26B | 27B total (MoE) | ~18 GB | 16 GB VRAM |
| Gemma 4 31B | 33B total | ~20 GB | 24 GB or more |
*A note on Gemma 4's naming: "E2B" and "E4B" refer to the text backbone size (2B and 4B), but each model also includes a vision encoder for image understanding — bringing total parameters to 5B and 8B respectively.
What's "quantization"? It's a compression technique — the model's precision is slightly reduced, shrinking file size dramatically with minimal impact on response quality. Always use quantized models (Q4_K_M is the sweet spot). You get ~90% of the quality at 30% of the VRAM cost.
What's MoE? Gemma 4 26B uses Mixture of Experts architecture — only a fraction of the 26 billion parameters are active at any moment during inference. This means it loads like a 26B model (~18 GB) but runs with the speed and VRAM efficiency of a much smaller one. It's why 16 GB VRAM can handle it while a dense 26B model would need 24 GB.
What "B" means: The B stands for billion — so 8B = 8 billion parameters. More parameters generally means smarter responses, but also more VRAM required.
CPU: "Good Enough" Is Good Enough
For GPU-accelerated inference, the CPU's job is basically just feeding data to the GPU. A mid-range CPU — Intel Core i5 or AMD Ryzen 5 — handles this without breaking a sweat.
Don't blow your budget here. Every dollar you spend on a fancier CPU is a dollar not spent on VRAM, and VRAM is what actually moves the needle.
System RAM: Just Don't Be Stingy
System RAM holds data temporarily while the GPU works. Rules of thumb:
- 16 GB — fine if you have a dedicated GPU and run one model at a time
- 32 GB — better if your model is larger than your VRAM (spill-over to RAM)
- 64 GB+ — if you're doing CPU-only inference with no GPU
If a model doesn't fully fit in VRAM, the overflow spills into system RAM. This is 5–20x slower than VRAM. You can still run it — just don't expect fast output.
Storage: NVMe SSD, No Exceptions
Model files are large. Gemma 4 E4B at Q4 is around 5 GB. The 26B MoE is ~18 GB. The 31B dense is ~20 GB. And if you keep multiple models around (which you will), storage fills up fast.
If you're loading these from a mechanical hard drive, you're waiting 30–60+ seconds every time you start a model. An NVMe SSD loads the same model in under 5 seconds.
Minimum recommendation: 1 TB NVMe SSD. If you plan to keep multiple models available to switch between, grab 2 TB.
Four Build Tiers — Pick Your Level
🟢 Starter Tier: No GPU Needed (~$0 extra if you already have a PC)
Who this is for: You just want to see what local AI feels like, verify the toolchain works, and you're not ready to spend money yet.
What you need: Any PC with 16 GB of RAM.
What you can run: Gemma 4 E2B — a 5B multimodal model (2B text backbone + vision encoder) that can run on CPU. Natively accepts image input, and handles basic Q&A, summarization, and simple tasks. With a dedicated 8 GB GPU, it runs noticeably faster.
Honest assessment: This tier is mainly for curiosity. Small models have real limitations on complex tasks. If you want to actually use local AI for real work, you'll need to step up.
Honestly, if you're at this tier, you might get better results installing OpenClaw and connecting it to a cloud API instead of running locally. Local deployment at this level is more "proof of concept" than daily driver.
🔵 Entry Tier: RTX 5060 Ti 16 GB (~$500–$600 for the GPU)
Who this is for: Anyone who wants smooth, daily-driver local AI without breaking the bank.
Why 16 GB and not 8 GB? The 8 GB version of the 5060 Ti runs 8B models (like Gemma 4 E4B) right at the edge of its VRAM limit. Open the model, run something else in the background, and you're crashing. The 16 GB version runs the Gemma 4 26B MoE model — a genuinely capable 26B model — with room to breathe. That jump in model quality is worth the extra cost.
Full entry-tier build (US market estimates):
| Component | Recommendation | Est. Price |
|---|---|---|
| CPU | AMD Ryzen 5 5600 | ~$120 |
| Motherboard | B550 ATX | ~$100 |
| RAM | 32 GB DDR4 (2×16 GB) | ~$200 |
| GPU | RTX 5060 Ti 16 GB | ~$549 |
| Storage | 1 TB NVMe SSD | ~$150 |
| PSU | 750W 80+ Gold | ~$80 |
| Case + Cooling | Mid-tower + air cooler | ~$100 |
| Total | ~$1,300 |
What you can run: Gemma 4 26B MoE — fast responses, handles coding help, writing assistance, and everyday Q&A without breaking a sweat. This is a legitimately capable model, not a toy.
What you can't run: Dense 31B+ models — not enough VRAM. Don't try to force it; performance will be miserable.
6-core · Budget CPU for AI builds
16GB GDDR7 · Best entry-level GPU for local AI
🟡 Mid-Range Tier: 16 GB or 24 GB VRAM ($750–$3,000 for the GPU)
This tier splits into two meaningful sub-levels based on what you actually want to run.
Option A — 16 GB VRAM (RTX 5070 Ti): Significantly faster inference than the entry tier for MoE models. The output speed jump is noticeable. Gemma 4 26B MoE runs well here. However, dense 31B models need 20+ GB, which means they'll overflow into system RAM on a 16 GB card.
Option B — 24 GB VRAM (RTX 4090): The RTX 4090 is no longer available new from Amazon — if you already own one, it handles the Gemma 4 31B dense model comfortably. For a new build targeting 24 GB+, the RTX 5090 32 GB is the current recommended option.
Full mid-range build (24 GB tier):
8-core · Best mid-range CPU for AI builds
| Component | Recommendation | Est. Price |
|---|---|---|
| CPU | AMD Ryzen 7 9700X | ~$320 |
| Motherboard | B850M | ~$180 |
| RAM | 64 GB DDR5 (2×32 GB) | ~$800 |
| GPU | RTX 4090 24 GB | ~$3,000 |
| Storage | 2 TB NVMe Gen4 SSD | ~$350 |
| PSU | 850W 80+ Gold | ~$120 |
| Case + Cooling | Mid-tower + air/AIO | ~$150 |
| Total | ~$4,920 |
🔴 High-End Tier: RTX 5090 32 GB (~$2,000+ for the GPU)
Who this is for: Power users, small businesses, and developers building private AI tools who need the best single-GPU experience available.
The RTX 5090's 32 GB of VRAM handles the Gemma 4 31B dense model with headroom to spare. You can also run highly compressed 70B models (Q2/Q3 quantization) from other families like LLaMA or Qwen, though quality takes a hit at those compression levels. For a truly smooth 70B experience, you'd need a multi-GPU setup or a professional card.
Pair it with: 64–128 GB DDR5 RAM, 2–4 TB NVMe storage, and a 1,200W PSU minimum. The 5090 pulls serious power — don't cheap out on the power supply.
32GB GDDR7 · Best single-GPU for local AI
Full high-end build:
12-core · High-end CPU for AI builds
| Component | Recommendation | Est. Price |
|---|---|---|
| CPU | AMD Ryzen 9 9900X | ~$450 |
| Motherboard | X870E | ~$300 |
| RAM | 128 GB DDR5 (4×32 GB) | ~$1,600 |
| GPU | RTX 5090 32 GB | ~$3,899 |
| Storage | 4 TB NVMe Gen5 SSD | ~$600 |
| PSU | 1,200W 80+ Gold Full Modular | ~$200 |
| Case + Cooling | 360mm AIO + premium case | ~$250 |
| Total | ~$7,300 |
Software: What Do You Actually Run the Models With?
Hardware sorted — now you need software to manage and run the models.
Ollama (recommended if you're comfortable with a terminal)
Install it, type ollama run gemma4:26b, and you're done. It downloads the model automatically and starts a conversation in your terminal. Fast, lightweight, supports almost every major open-source model, and has a huge community. This is the standard for local AI deployment in 2026.
LM Studio (recommended for beginners)
A full desktop app with a proper UI. Browse models, download with a click, switch between them in a dropdown, and start chatting. The interface shows real-time VRAM usage so you can instantly see whether your hardware can handle a given model. Zero command line required.
Both are free. Ollama is faster and more flexible; LM Studio is easier to get started with.
Common Mistakes to Avoid
Why Not AMD GPUs?
AMD cards often have more VRAM per dollar — which sounds great on paper. The problem is the software ecosystem. Local LLM tools are built around NVIDIA's CUDA platform. AMD uses ROCm, which works, but it's essentially a compatibility layer. You lose roughly 20% of performance versus equivalent NVIDIA hardware, new models sometimes don't support ROCm at launch, and when something goes wrong, troubleshooting is significantly harder.
NVIDIA's premium pricing is frustrating — but for local AI, the ecosystem advantage is real.
What About Apple Silicon Macs?
A Mac with 32–96 GB of unified memory is a genuinely solid option for local AI in 2026. Apple's unified memory means the full RAM pool is available for model loading — a 32 GB Mac can load Gemma 4 26B or 31B without issues.
The tradeoff: Unified memory bandwidth (even M5 Max at 614 GB/s) is lower than dedicated GDDR7 VRAM on a 5090 (1,792 GB/s). Models load fine but generate tokens more slowly than a comparably priced PC GPU setup.
April 2026 recommendation: If you're already in the Apple ecosystem or want a portable machine, a Mac with 32 GB+ unified memory is a legitimate choice — especially for personal use. If you're building hardware specifically for local AI performance, a PC with a dedicated GPU wins on price-to-performance.
Why Not "Modded" GPUs?
You'll occasionally see GPUs with doubled VRAM for sale — an RTX 2080 Ti with 22 GB instead of 11 GB, for example. These are aftermarket VRAM chip swaps done by third parties.
Skip them. The VRAM chips are often sourced from used mining cards that have already logged thousands of hours. Build quality is inconsistent. There's no manufacturer warranty. And when they fail — they tend to fail hard, not gracefully. The "savings" usually aren't worth it.
The Bottom Line
You don't need to spend $5,000 to run a useful local AI. A solid entry-level build around $1,300 runs the Gemma 4 26B MoE model smoothly — and that's a genuinely capable model for daily writing assistance, coding help, and Q&A.
If you want to run the dense 31B model — where quality takes another step up — plan for 24 GB of VRAM. That's the real threshold.
Hardware prices change frequently. The estimates above reflect US market prices in early 2026 and should be used as ballpark figures.