@MinLiBuilds: A new cost-effective benchmark has appeared with Qwen3.8-27B. AA Index 52.0 score, ranking 108/137 models in capability. If the price is cheaper than theirs, it's a local kill line. The local cost formula is simple: Cost per Token = (Total machine price + 3-year electricity cost) ÷ 3-year total output Tokens…

A new performance threshold has emerged with Qwen3.8-27B.

Scoring 52.0 on the AA Index, it dominates 108 out of 137 models on the capability leaderboard.

If priced lower than competitors, it becomes an on-the-spot cost killer.

The local cost formula is just one line:

Cost per Token = (Total system price + 3-year electricity cost) ÷ Total Tokens produced over 3 years

Total Tokens over 3 years = 26,280 hours × Utilization rate × 3,600 seconds × Tokens per second throughput.

The single variable that truly determines cost: utilization rate.

With fixed costs, higher usage means cheaper per-token economics.

The following figures are pure speculation, extrapolated based on values the community might realistically achieve—doesn’t affect the viewing experience.

Taking an RTX 5090 as an example:

Full system cost: 35,000 RMB, running 27B 4-bit quantized, high-concurrency total throughput at 800 tok/s, electricity at 0.6 RMB/kWh, with 3-year depreciation.

If used only 2.4 hours per day (10% utilization):

Output cost equivalent to $0.697 per million tokens.

Assuming Opus 4.6 is still available, referencing Opus 5’s pricing.

That’s 36 times cheaper than Opus 4.6.

If used 4.8 hours per day:

$0.356 per million tokens.

70 times cheaper.

Here’s the key point.

If you run the 5090 as a service with 70% utilization:

It can output 53 billion tokens over 3 years, total cost around 42,000 RMB.

This doesn’t account for concurrency limits without KV cache, nor throughput losses from longer contexts causing batch size reductions.

Selling at the price of the cheapest comparable cloud model, theoretical gross margin reaches 91%, paying back in about 72 days under full load.

Of course, this is just theoretical.

To push it further:

Even at just 2.4 hours daily usage, local deployment remains about 18 times cheaper than Opus 4.6.

This is the truly terrifying part about 27B Dense models:

They’re already powerful enough.

But token costs? They’re not even in the same league anymore.

Thread2 contains more parameter comparisons.

The experience on DGX Spark is a completely different story.

After sending a task, you might stare at a blank screen for ages before seeing the first answer character.

The entire task takes forever to complete.

This is no longer interactive reasoning—it’s more like batch processing.

So machines like DGX Spark, with massive unified memory but relatively limited bandwidth, are actually better suited for MoE models like Ling-3-Flash.

Total parameters can be huge, but the actual parameters activated per token are far fewer.

Just because you can fit a Dense model in doesn’t mean it’s suitable for running.

Being able to run it versus running it smoothly are two different things.

If you want to see how to deploy MoE or Dense models locally step-by-step, along with detailed instructions for integrating Claude Code, see below.

When machine costs are fixed, amortizing them over 3 years means the more you use it, the more tokens you produce, and the cheaper the amortized cost becomes.

The harder you use it, the faster you recover the investment.

Here are some theoretical values for reference, excluding infrastructure-related investments.

Previously in construction, I specialized in calculating infrastructure electricity costs.

Dario and Sam’s trillion-dollar valuations are about to be challenged.

I’ve done performance evaluations before, you can check those out.

Prices went up again? I was still thinking 26,000 per card.

Just inquired about a dual-card system today, a bit scared—the seller said they’d give me a quote tomorrow.

Nice, dual cards allow self-sufficiency. With a proper workflow, you can generate unlimited videos for free.