Local LLM hardware guide 2026: VRAM and RAM you actually need from 7B to 70B.

💡 What You Will Learn

Local LLM hardware guide 2026: VRAM and RAM you actually need from 7B to 70B.

📜 Table of Contents

• 1. The estimation formula
• 2. Hardware by model size
• 3. KV cache: the hidden VRAM sink
• 4. Three hardware routes
• 5. Three budget tiers
• 6. Test before you buy
• FAQ

Local LLM Hardware Guide 2026: What 7B to 70B Models Actually Need

Two numbers decide whether a local model runs: how much VRAM the weights take, and how much VRAM your GPU has. This guide lines up model size, quantization and hardware, then gives three budget tiers.

1. The estimation formula

VRAM ≈ parameters x bytes per parameter + KV cache + framework overhead

Quantization is the cheapest way to buy VRAM: the same card that runs 7B at FP16 fits 13B at Q4. Exact file sizes vary - check the model card.

2. Hardware by model size

Downgrade order when VRAM is short: smaller quantization, then shorter context, then offloading layers to RAM (much slower), then a smaller model.

3. KV cache: the hidden VRAM sink

Longer context means a bigger KV cache - on a long-context run it can eat several GB more than the weights. Keep context as short as the task allows, or enable quantized KV cache if your runtime supports it (check the framework docs).

4. Three hardware routes

5. Three budget tiers

Prices move - check current retail listings. A used 12 GB card is usually the best value, if your PSU and case can take it.

6. Test before you buy

1. Run the smallest model on your current machine to prove the toolchain works
2. Rent a cloud GPU of the target class by the hour, run your real model and context length, note peak VRAM and speed
3. Only then buy - this step prevents paying for hardware that cannot run your workload

FAQ

Can 16 GB RAM run 7B? Yes, Q4 weights take 4-6 GB, but reserve headroom for the OS and context; 16 GB RAM plus 8 GB VRAM is a sane floor.

Can VRAM and RAM be combined? Partially - some frameworks offload layers to RAM at a large speed cost. Fine occasionally, not for daily use.

Why does an 8 GB card handle image models but choke on LLMs? Different memory profiles: diffusion is a single forward pass, an LLM keeps a growing KV cache.

Buy a GPU or rent cloud first? Rent if you are unsure. A few dozen hours of cloud rental costs far less than the wrong card.

Note: VRAM and speed figures are community rules of thumb; exact numbers vary by model, quantization and runtime - see the model card and framework docs.

❓ FAQ

Can 16 GB RAM run a 7B model?

Yes - Q4 weights take 4-6 GB. Pair it with at least 8 GB VRAM, otherwise long contexts get painfully slow.

Can I make up for low VRAM with system RAM?

Some runtimes offload layers to RAM, which costs several times the speed. Acceptable for occasional runs, not daily work.

Should I buy a GPU or rent cloud first?

Rent if you are unsure: a few dozen rental hours cost far less than buying the wrong card.