Every week the pitch gets louder: buy a $4,699 box, load it with 128GB of unified memory, and never pay an API bill again. The hardware is real, the memory prices are real, and the pitch is also, in most cases, wrong. The math is not complicated, but it depends on one variable that most comparisons leave out: which cloud model you are actually replacing.
This piece works the numbers end to end, using published list prices as of September 2026. No estimates dressed as facts, and where we assume something, we label it.
The hardware, at list price
Three machines define the local AI market right now:
| Machine | Price | Memory | Bandwidth | Claims |
|---|---|---|---|---|
| Mac Studio M5 Max | $2,499 | up to 128GB | 614GB/s | 70B models at chat speed |
| Mac Studio M5 Ultra | $5,499 | up to 512GB | 1.2TB/s | frontier-size models locally |
| NVIDIA DGX Spark Founders Edition | $4,699 | 128GB | 273GB/s | up to 200B parameters |
The DGX Spark launched at $3,999 in October 2025 and NVIDIA raised the MSRP to $4,699 in February 2026, citing memory supply constraints. Apple's M5 Ultra lineup is covered in detail in our Mac Studio M5 Ultra breakdown, and AMD's answer, the Ryzen AI Max Pro 400 with 192GB, is analyzed in our Ryzen AI Max Pro 400 piece.
The API side, at list price
| Model | Input / 1M | Output / 1M | Source |
|---|---|---|---|
| Claude Opus 5 | $5 | $25 | Anthropic announcement |
| Claude Sonnet 5 | $2 | $10 | Anthropic platform docs |
| GPT-6 Astra | $10 | $50 | OpenAI API pricing |
| DeepSeek V4-Pro (off-peak) | $0.66 | $1.98 | DeepSeek API docs |
| DeepSeek V4.1 Flash (off-peak) | $0.15 | $0.60 | DeepSeek API docs |
The spread between the top and the bottom of this table is roughly 40x on output tokens. That spread is the whole story, and most "local AI saves you money" takes quietly assume they are replacing the top row.
The break-even math
Take a heavy individual workload: 100 million output tokens per month, plus 200 million input tokens. That is roughly what a full-time developer running agentic coding tools all day consumes, and agent sessions skew output-heavy because reasoning traces and generated code dominate the bill.
Replacing Claude Opus 5: 100M output at $25 plus 200M input at $5 comes to $2,500 + $1,000, so $3,500 per month. A $5,499 M5 Ultra pays for itself in under two months. Even the $2,499 M5 Max pays back in under a month.
Replacing GPT-6 Astra: $5,000 + $2,000, so $7,000 per month. Payback in weeks.
Replacing DeepSeek V4.1 Flash: $60 + $30, so $90 per month. The M5 Ultra takes 61 months, over five years, to match what it costs. The DGX Spark takes 52 months. Against the cheapest APIs, the hardware case collapses entirely.
Replacing Sonnet 5: $1,000 + $400, so $1,400 per month. Payback in roughly four months on the M5 Max.
Here is the honest summary: local AI is not a bet against cloud computing. It is a bet specifically against premium closed-model pricing. If your workload tolerates a frontier open-weights model at $0.60 per million output tokens, the $4,699 box is money you will never earn back in electricity alone.
The costs the pitch leaves out
Electricity. Assume (labeled assumption) each box averages 120W under sustained inference load. Running 24/7, that is about 88 kWh per month, roughly $20 to $30 at typical US or Western European residential rates. Not a dealbreaker, but it means the DeepSeek-comparison payback gets worse, not better.
Throughput ceilings. The M5 Ultra's 1.2TB/s bandwidth supports something like 30 to 40 tokens per second on a 70B model at 4-bit quantization, per our Mac Studio M5 Ultra breakdown. At 35 tok/s, one stream running 24/7 produces about 90 million tokens per month, which on paper nearly covers our 100M-output workload. The catch is that agentic work is not one continuous stream: it is many concurrent sessions, and every additional stream divides the per-stream rate. Two streams halve it, four quarter it, and interactive latency degrades with each. The cloud has no such ceiling, and rate limits cost extra rather than degrading everyone.
Quality substitution. A local 70B quantized model is not Claude Opus 5. The payback math above assumes you can swap them, and for many coding and reasoning tasks you cannot yet. The realistic framing is that you are buying a different, cheaper product, not the same product for free. Cache economics narrow the gap too: DeepSeek's cached input rate of $0.003 per million tokens, analyzed in our DeepSeek V4.1 Flash cost study, makes repetitive agent workloads nearly free in the cloud, exactly the workload local hardware proponents cite as their best case.
Depreciation and capital risk. API prices fell roughly 20% in a single OpenAI cut this August, and DGX-class hardware will not get more valuable while memory prices and model pricing both keep moving.
Our Read
The cost case for local AI is a spread trade, not an absolute one. It pays off precisely when two conditions hold at once: you consume at frontier-API volume (thousands of dollars monthly, not hundreds), and the model class you can run locally is good enough for the work. Condition one rules out most individuals. Condition two rules out most of the people for whom condition one holds, which is why frontier labs still sell so many API tokens.
Where local genuinely wins today is not cost at all. It is the line items that never appear on a token invoice: documents that cannot legally leave a building, prototypes that need unlimited iteration at odd hours, and products whose margins cannot survive a per-call tax. Buy the box for those reasons. Buy it for the token math only if you are paying Opus or Astra rates at scale, and then the machine really does pay for itself in weeks.
If your workload is mostly retrieval and rewriting rather than raw generation, the cheaper lever usually is not hardware at all, as we argued in our RAG versus fine-tuning guide.
References
- Anthropic, "Introducing Claude Opus 5," July 2026. https://www.anthropic.com/news/claude-opus-5
- NVIDIA, "DGX Spark Arrives for World's AI Developers," October 2025. https://nvidianews.nvidia.com/news/nvidia-dgx-spark-arrives-for-worlds-ai-developers
- AMD, "Ryzen AI Halo for AI Developers." https://www.amd.com/en/products/processors/desktops/ryzen/ryzen-ai-halo.html
- DeepSeek API documentation, Models and Pricing. https://api-docs.deepseek.com/quick_start/pricing