Blog
Local AI hardware, explained
Guides and comparisons for picking the right hardware to run and fine-tune LLMs locally — grounded in VRAM math, real model requirements, and total cost of ownership.
Local LLM Hardware: The Complete Guide (2026)
The complete guide to choosing hardware for running LLMs locally — GPU VRAM, quantization, Mac vs PC, cost vs cloud, and which build fits your use case.
Read moreBest GPU for Running Local LLMs in 2026
The best GPU for local LLMs is the one with enough VRAM for your model. Our 2026 picks by tier, plus why used 3090s win on value.
Read moreMac vs PC for Local AI: Which Should You Buy in 2026?
Mac vs PC for local AI in 2026: unified memory for big-model inference versus NVIDIA VRAM, raw speed, and CUDA fine-tuning. A clear buying guide.
Read moreThe Cheapest Way to Run a 70B Model Locally
The cheapest way to run 70B locally is a single used 3090 at Q3, but two 3090s at Q4 is the value sweet spot. Costs and tradeoffs.
Read moreBest Budget AI Workstation Builds for 2026
A budget AI workstation guide for 2026: spend on VRAM first, then RAM, then CPU, across three honest price tiers for running 8B to 70B models locally.
Read moreBest Mac for Running LLMs Locally (M-Series Guide)
The best Mac for local LLMs comes down to unified memory, not GPU cores. M4, M4 Pro, M4 Max, and Ultra tiers explained by model size.
Read moreAMD vs NVIDIA for Local AI: Does ROCm Compete Yet?
AMD vs NVIDIA for local AI: NVIDIA's CUDA is still the safe default, but AMD Radeon now does inference well via ROCm and Vulkan. Where each wins.
Read moreIs the RTX 5090 Worth It for Local LLMs?
The RTX 5090's 32GB GDDR7 and ~1.8TB/s bandwidth make it the fastest single consumer card for local LLMs — worth it for headroom and speed, if you can get one.
Read moreHow Much VRAM Do You Need to Run Llama 70B?
The VRAM to run Llama 70B: about 40GB for a 4-bit quant. See the math, a quant-by-quant table, and what hardware hits each tier.
Read moreLLM Quantization Explained: Q4 vs Q8 and What to Pick
LLM quantization explained: how Q4 vs Q8 GGUF levels trade VRAM for quality, and which one to pick for your hardware.
Read moreOllama vs LM Studio: Which Local LLM Runner Should You Use?
Ollama vs LM Studio: Ollama is the scriptable CLI and server, LM Studio is the polished GUI. Both wrap llama.cpp, so speed is similar. How to choose.
Read moreWhat Hardware Do You Need to Run DeepSeek Locally?
Hardware to run DeepSeek locally: the full 671B R1 needs a datacenter, but distilled 1.5B–70B versions run on consumer GPUs. VRAM by variant.
Read moreBest Laptop for Running LLMs Locally (2026)
The best laptop for local LLMs is a high-memory MacBook Pro — unified memory beats the 8–24GB VRAM cap on Windows gaming laptops. Tiers and picks.
Read moreRTX 4090 vs 3090 for AI: Is the Upgrade Worth It?
RTX 4090 vs 3090 for AI: both have 24GB, so they run the same models. The real difference is speed, efficiency, and price.
Read moreCloud GPU vs Buying a Workstation: When Each Wins
Cloud GPU vs buying for AI: the break-even math, when renting beats a workstation, and the hidden costs both sides hide.
Read moreSingle vs Dual GPU for LLM Inference: Do You Need Two Cards?
When dual GPU for LLM inference is worth it: pool VRAM to fit bigger models, not chase linear speed. Capacity-first decision guide.
Read moreTokens Per Second Explained: How Fast Is Fast Enough?
Tokens per second LLM speed explained: prefill vs decode, the human reading benchmark, and which hardware spec actually predicts generation speed.
Read moreHow to Run Llama 3 Locally: A Beginner's Hardware Guide
Learn how to run Llama 3 locally: the fastest beginner path, plus the hardware each model size actually needs.
Read moreWhat Hardware Do You Need to Fine-Tune an LLM?
Hardware for fine-tuning LLMs: VRAM rules of thumb for full, LoRA, and QLoRA, why training beats inference, and when to rent an H100.
Read moreBuilding a Self-Hosted LLM Server for Your Team (vLLM)
How to build a self-hosted LLM server for teams: vLLM concurrency, KV cache VRAM headroom, dual-GPU 70B sizing, and cost vs cloud APIs.
Read morePower and Cooling for Multi-GPU AI Rigs
Power and cooling for multi-GPU AI: PSU sizing for 1-4 GPUs, transient spikes, blower vs liquid cooling, slot spacing, and the wall-circuit ceiling.
Read more