TL;DR: You can run Llama 3 locally for as little as $250. But what you get depends heavily on the tier. This guide breaks down exactly what each price point buys you — and sets honest expectations about how local LLMs compare to ChatGPT.
Bottom line: $800 (used RTX 3090) is the value king. $599 (Mac Mini M4) is the best plug-and-play option. $250 (Arc B580) works if you're okay with limitations.
The Expectations Gap (Read This First)
Running a local 8B model is not the same as using GPT-4 or Claude 3.5 Sonnet. An 8B model (8 billion parameter model — the number roughly correlates with capability and resource requirements) is more like a capable intern than a senior expert. It can draft emails, answer factual questions, and summarize documents reasonably well. It won't nail complex reasoning tasks or nuanced writing the way a frontier model does.
That said, local AI has real advantages: it's private, it's free after hardware costs, it runs offline, and it's fast enough to be useful for many workflows.
Knowing this going in matters. If you expect ChatGPT, you'll be disappointed. If you want private, offline inference for practical tasks, you'll be happy.
Tier 1: $250 — The Entry Point
Intel Arc B580 (~$250)
12GB VRAM, 456 GB/s bandwidth. Runs 7B and 8B models at usable speeds — around 35–38 tok/s on Llama 3 8B Q4_K_M using Ollama.
What it handles: Chat assistants, summarization, simple coding help, document Q&A at 7B–8B scale.
What it doesn't handle well: 13B+ models (technically possible at Q3/Q2 but slow and lower quality), anything requiring broad software compatibility.
Setup: Install Ollama, pull llama3.1:8b, run it. That's it. Works on Windows with Arc drivers.
Good fit for: Budget builders who want to try local AI before committing more money. Also decent for a dedicated AI box running smaller models 24/7. For more options in this price range, see our full budget GPU roundup under $300.
Want a deeper look at the B580 versus its competitors? See the 16GB GPU comparison.
Tier 2: $599 — The Sweet Spot (If You're in the Apple Ecosystem)
Mac Mini M4 (16GB unified memory, $599)
Apple Silicon's unified memory architecture means the GPU and CPU share the same memory pool. 16GB unified handles 7B–13B comfortably. Larger than what a discrete 16GB GPU can run because Apple's memory architecture is more efficient for this.
Speed: ~30 t/s on Llama 3 7B Q4_K_M. Not blazing, but usable. Noticeably slower than the RTX 3090 on the same models.
What it handles: 7B–13B models well. The 22B Mistral at Q4 is borderline — playable but slow.
Why it wins at this tier: Zero setup friction. Install Ollama, it works. Native support for llama.cpp. Quiet, energy-efficient, runs 24/7 without issue. No driver drama.
Who it's for: Mac users who already live in the Apple ecosystem. If you're on Windows, the RTX 3090 at $800 is a better value.
Tier 3: $800 — The Value King
Used RTX 3090 (~$800)
24GB GDDR6X, 936 GB/s bandwidth. This is where local AI gets genuinely good. At 24GB you can run:
- Llama 3.1 13B at Q4_K_M comfortably (~65 t/s)
- Llama 3.1 70B at Q2_K (slower, quality reduced, but functional for testing)
- CodeLlama 34B at Q4_K_M (~30 t/s — excellent for coding)
- Mistral 22B at Q4_K_M at full quality
Benchmark: ~93 t/s on Llama 3.1 8B Q4_K_M. Nearly as fast as the 4090 for inference, for half the price.
The catch: It's a used card. PCIe power connectors, resale market variability, and no warranty. Buy from a reputable seller (eBay with buyer protection, or r/hardwareswap with verified sellers). Check for mining history — it matters for longevity.
Who it's for: Anyone who wants the best value and is comfortable building a PC. This is the recommendation for most people who are serious about local AI. If budget isn't the constraint and you're eyeing the flagship tier, see how the RTX 5090 compares to the 4090 for local AI.
Quick Tier Summary
- $250 (Arc B580): Runs 7B–8B. Entry-level. Some software friction.
- $599 (Mac Mini M4): Runs 7B–13B. Best plug-and-play. Apple only.
- $800 (used RTX 3090): Runs 13B–34B comfortably. Best value overall.
Not sure which model sizes make sense for your use case? Our VRAM guide breaks it down by model.
For a full breakdown of the GPU options across all price ranges, see our complete GPU comparison.