What Would It Cost to Run Claude Opus Locally?

You cannot run Claude Opus locally. Anthropic has never published Opus weights. The model is served through Anthropic’s API and apps only, and no GPU purchase changes that. Anyone selling you a “local Opus” setup is selling you something else.

The real question underneath is worth answering: what hardware do you need to run a model in that class at home, what does it cost, and does it beat a subscription? That is arithmetic, and the numbers are not friendly.

1. Why the literal answer is no

Anthropic distributes Claude through the Claude apps and the API. There is no weights download, no Hugging Face repo, no GGUF. Closed weights are a product decision, not a hardware limit. A rack of H100s in your basement still cannot load a file that does not exist publicly.

So the honest reframe is: run an open-weight model of similar capability, on hardware you own. That part is real, and the price is knowable.

2. What you can actually run instead

Frontier open weights in 2026 got bigger, not smaller. Most of the well-known releases left the desk entirely.

Two numbers do different jobs here. Total parameters set your memory bill, because every weight must sit in RAM or VRAM. Active parameters set your speed, because only those get multiplied per token. That is why a 118B Mixture-of-Experts model with 8B active runs at small-model speed on a Mac, while a dense 70B crawls on the same box.

Details and quant tables: Laguna S 2.1 local setup and why Kimi K3 does not fit.

Be clear about what you are getting. None of these is Opus. The closest realistic desk setup is a strong open coding and agent model, not a copy of the model you pay Anthropic for.

3. The hardware math

Pick your target model, then buy memory to hold it plus a KV cache. Prices below are August 2026, and the 2026 DRAM shortage moved all of them.

Apple discontinued the 256GB and 512GB Mac Studio configurations in 2026, so the 96GB M3 Ultra is the top unified-memory Mac you can buy today. That single change removes the machine most people used to point at for “big model at home”.

Three warnings before you spend:

• Fits is not runs. A 128GB Ryzen AI Max+ 395 box holds a dense 70B at Q4 and decodes it at about 5 tokens per second, because the memory bandwidth is roughly 256 GB/s. The same box runs sparse MoE models fast. Buy for the architecture you plan to run.
• The advertised price is often a queue. Strix Halo boxes ship as pre-orders and sell out constantly. See which Strix Halo mini PC to buy.
• Unified memory is soldered. The capacity you buy is permanent on every one of these machines.

4. The break-even

Anthropic’s published consumer prices as of August 2026: Pro is $20 per month, or $17 per month billed annually at $200 upfront. Max is $100 per month for 5x Pro usage and $200 per month for 20x (claude.com/pricing).

Now the local side, priced honestly: hardware cost minus resale, spread over the months you keep it, plus power. The US average residential electricity rate is about $0.18/kWh as of August 2026 (18.44 cents, up about 6% year over year, per state rate tracking of EIA data). Your state rate is what matters: it runs from about 12 cents in Idaho to 52 cents in Hawaii.

The 128GB row uses our own resale assumption, so treat it as an estimate and not a quote. The other rows come straight from the electricity break-even math.

Read the table plainly. Nothing here beats $20 a month on cost. The cheapest credible local setup lands near $36 to $55 a month all-in, running a weaker model. A 128GB box is in the same monthly range as Max 5x at $100 only if you keep it three years and it holds half its value.

There is one number that ends most of these arguments. OpenRouter lists Qwen3.8-27B at $0.40 per million input tokens, so $46 of monthly electricity alone buys about 115 million input tokens of a comparable open model, before you have paid for any hardware. Very few individuals consume that.

5. When local actually wins

Cost is the wrong reason. These are the right ones.

1. Privacy and data control. Client code, health records, legal documents, anything under a contract that forbids third-party processing. No subscription solves this. Local does.
2. Always-on agents. A loop that runs overnight burns tokens with no human watching. Owning the hardware removes the per-token anxiety, and usage behaviour changes once the meter stops.
3. Offline and air-gapped work. No network, no vendor outage, no rate limit at 2am.
4. Fine-tuning and experimentation. Unmetered iteration is worth more than it looks on a spreadsheet.
5. You keep an asset. Hardware has resale value. Subscription dollars do not. That is a real argument, but it is an asset argument, not a savings argument.

Local loses when you want the strongest model on hard reasoning, when you want it now with no setup, and when you are honestly a light user. The community advice is blunt and correct: max out your subscriptions first, then find out whether you hit a wall they could not solve.

The short version

• You cannot run Claude Opus locally. Closed weights, full stop.
• The closest thing that fits a desk is Laguna S 2.1 at 73.1GB, or gpt-oss-120b. Both need a 96-128GB machine.
• That machine costs $2,299 to $4,699 in August 2026, or $5,299 and up for a Mac Studio.
• All-in monthly cost lands near $36 to $55 for a used-GPU rig, versus $20 for Pro and $100 for Max 5x.
• Buy local for privacy, always-on agents, and offline work. Do not buy it to save money, because at 2026 prices it does not.

Related reading: is the DGX Spark worth it, Kimi K3 locally, the electricity break-even.