Run Large Language Models on Your Own PC: A Scientist’s Guide to CPUs, GPUs, RAM, VRAM & Quantization 🚀
“Give me a GPU big enough and a model quantized enough, and I shall inference the world.” — Archimedes, probably
Why read this?
If you’ve ever asked yourself:
“Can I run a GPT‑style model on my rig without mortgaging the cat?”
…this article is for you. We’ll dissect the five hardware pillars that decide whether your local LLM soars or sputters:
| Pillar | TL;DR |
|---|---|
| CPU | General‑purpose brain; great at many things, master of none. |
| GPU | Vector/matrix powerhouse; crunches m × x + b millions of times per second. |
| RAM | Short‑term memory for all running programs. |
| VRAM | GPU‑attached RAM; the model’s penthouse suite. |
| Quantization | Shrinks model weights (16 → 8 → 4 bits) so they fit into the suites above. |
1️⃣ CPU vs GPU — Same Goal, Very Different Brains
| Feature | CPU | GPU |
|---|---|---|
| Cores | Few (8‑32) complex cores | Hundreds‑thousands of simple ALUs |
| Optimized for | Branching, OS tasks, scalar math | Parallel matrix ops & graphics |
| Example workload | Sorting, web browser, OS interrupts | 4 096 × 4 096 GEMM for a transformer layer |
| Why it matters for LLMs | Handles tokenizer, I/O, orchestration | Runs the attention & MLP math |
Key takeaway: You can infer on a CPU, but you’ll wait ✈️. A mid‑range GPU slashes token times from seconds to milliseconds.
2️⃣ RAM — Where Everything Takes a Coffee Break
Role: Holds the model weights and your OS, browser, Spotify, etc.
Recommended for local LLMs
| Minimum | Sweet Spot | Power‑User |
|---|---|---|
| 16 GB DDR4/5 | 32‑64 GB | 96 GB+ for massive experiments |
Pro tip: Leave at least 4‑6 GB free for the OS. Linux swap + zram can rescue you in an emergency, but paging 20 GB to disk will feel like dial‑up.
3️⃣ VRAM — The GPU’s Penthouse Suite
Role: Stores the active tensors during inference/training. Closer = faster (PCIe ≪ on‑package HBM).
| Card tier (2025) | VRAM | What fits?* |
|---|---|---|
| RTX 4060 / RX 7700 | 8‑12 GB | 3‑4 B models @ 4‑bit |
| RTX 4070 Ti SUPER | 16 GB | 7‑8 B @4‑bit or 13‑B @8‑bit w/ offload |
| RTX 5090 / RX 8900 | 24‑36 GB | 13‑34 B full 8‑bit |
| Prosumer Hopper H200 | 80‑144 GB HBM3e | 70‑B full precision 😎 |
*Rough rule of thumb: size ≈ (model params × bits) / 8 + activation overhead.
4️⃣ Quantization — Weight‑Watchers for AI Models
| Precision | Memory per parameter | Typical perplexity hit |
|---|---|---|
| FP16 | 2 bytes | Baseline |
| INT8 | 1 byte | +0‑2 pp depending on calibration |
| Q4 (4‑bit) | 0.5 byte | +1‑4 pp (still chatty!) |
Real‑world shrink‑ray 🌠
70‑B Llama‑2 FP16 : 140 GB
70‑B Llama‑2 INT8 : 70 GB
70‑B Llama‑2 Q4 : 35 GB ✅ Fits on a 48 GB RTX 5090
Quantized weights + ggml / GPTQ / AWQ loaders = laptop‑level inference.
5️⃣ Quick Compatibility Checklist
| Step | Command | What you’re checking |
|---|---|---|
| CPU info |
lscpu / wmic cpu get name
|
AVX2 / AVX‑512 for CPU back‑ends |
| RAM free |
free -h / Task Mgr |
≥ 16 GB available |
| GPU & VRAM |
nvidia-smi / rocm-smi
|
CUDA 12.x? HIP? VRAM amount |
| Driver & Toolkit | nvcc --version |
Matches PyTorch / TensorRT build |
6️⃣ From Theory to Tokens: Your First Local Run
- Install Ollama (Mac/Win/Linux) or Text‑Generation‑WebUI.
- Pull a quantized model
ollama pull llama2:7b-chat-q4_K_M
- Infer
ollama run llama2:7b-chat-q4_K_M
-
Watch VRAM with
nvidia-smi dmon— you’ll see ~6 GB used instead of 25 GB.
7️⃣ FAQ Speed‑round 🔄
| Question | Short answer |
|---|---|
| Can I chain GPUs? | Yes, via tensor / pipeline parallelism or vLLM offload, but consumer apps rarely support it out‑of‑the‑box. |
| Is Intel ARC good enough? | Now that oneAPI supports BF16 & INT8, ARC 870 / 880 can handle 7‑B Q4 models. |
| Does Apple M‑series need VRAM? | Unified memory is VRAM; aim for M2 Pro 32 GB+ for 13‑B Q4. |
Conclusion — Hardware matters, knowledge matters more
Running state‑of‑the‑art LLMs locally is no longer sci‑fi; it’s a weekend project when you:
- Match model bits to VRAM via quantization.
- Give your GPU room to breathe with adequate system RAM.
- Jump into tooling (Hugging Face, Ollama, LM‑Studio) that abstracts the heavy lifting.
Stay tuned — in the next post we’ll benchmark 4‑bit vs 8‑bit inferencing on a 4070 Ti SUPER and show you how to fine‑tune Dolphin‑Mixtral on 32 GB of system RAM. 🎣
✍️ Written by: Cristian Sifuentes — Full‑stack dev & AI tinkerer. Dark themes, atomic commits, and the belief that every rig deserves its own language model.
Happy local inferencing! 🚀💬
Tags: #LLM #GPU #Quantization #EdgeAI #Python #HuggingFace #Ollama
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