Possibly—but the reported “three-quarters” result is a single experiment, not a forecast for your workload. Rob Hill of Fortitude Omnis Group reported that a fine-tuned local model on an RTX 3080 Ti handled 73.0% of 1,000 Banking77 support-message decisions, with uncertain cases sent to Claude. The hybrid result was less accurate than Claude alone in that run. To find out whether the trade-off works for you, compare local-only, Claude-only, and hybrid results on the same representative, labeled examples.
What the reported three-quarters result does—and does not—show
In an article published September 29, 2026, Rob Hill of Fortitude Omnis Group described one run on the Banking77 support-intent dataset: 1,000 messages, one RTX 3080 Ti, and one night. A fine-tuned local “Laya” decision model handled 73.0% of decisions; the remaining uncertain cases went to Claude. The reported hybrid accuracy was 93.3%, compared with 94.2% for Claude alone. These are the author’s reported results, not an independent replication. Read the indexed article excerpt.
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The reported cost comparison was £3,898 versus £1,054 per million decisions. Hill described these figures as estimates based on published list prices, not invoice totals. They should not be read as realized savings or as a direct comparison of operating costs for another organization.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →There is also an important baseline distinction: Hill wrote that Claude’s answers came from an interactive Claude Code session working through batched answer sheets, not from the Claude API. So the result is not an API benchmark. The excerpt does not establish the precise Laya model version, training recipe, prompt, uncertainty threshold, routing implementation, or GPU utilization. Those gaps mean the experiment cannot be reproduced exactly from the available details.
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How to measure your own workload
Treat the reported local-handled share as a hypothesis. Build a test that answers two separate questions: how many examples can your local model handle, and what quality, speed, and cost do you get when it does?
- Define and freeze the task. Write down the category list, prompt, required output format, and input set before comparing systems. Assemble representative examples with trusted labels, keeping a held-out set that was not used to tune or fine-tune the local model. Reusing tuning examples can make its measured accuracy look better than its performance on new messages.
- Record the exact configurations. Run the same held-out examples through the local model alone and the Claude baseline. Log the local model identifier and version, quantization or other configuration, inference software, hardware, prompt, and decoding settings. For Claude, record the selected model ID and the date: Anthropic’s model roster changes, and its model overview links to current model information.
- Evaluate the hybrid routing policy. Apply a stated rule for deciding when the local model answers and when a case falls back to Claude. On the same examples, count local decisions and Claude fallbacks, then score the combined answers against the labels. Report local-handled share alongside overall accuracy and error types; a high local share by itself says nothing about whether the answers are acceptable.
- Measure speed under realistic load. Capture end-to-end latency and throughput for all three options, using the batch sizes and concurrency your workload is expected to use. Include fallback time in the hybrid measurement. A community benchmark repository is one example of tracking output speed, time to first token, and power across local llama.cpp/Ollama and hosted Claude API adapters; it is a measurement example, not evidence that its results transfer to your hardware or task.
- Compare costs on the same accounting basis. For hosted usage, use the applicable model and token pricing assumptions, and check actual usage rather than relying on a generic estimate. For local inference, state how you account for hardware, electricity, and amortization. Anthropic’s platform supports monitoring usage and cost by model and API key; Hill’s published-list-price estimate was not based on invoices. Anthropic usage and cost monitoring describes the platform context.
- Repeat on more than one slice. Test additional representative data slices and report variability and failure cases. A single run can conceal changes in class mix or input difficulty. Keep the reported local share, errors, latency, throughput, and cost assumptions attached to each result.
Compare the three options on the same examples
| Option | Quality measure | Operational measure | Cost measure |
|---|---|---|---|
| Local model only | Held-out classification accuracy and error types | Latency, throughput, hardware fit, and power | Hardware and operating cost under declared assumptions |
| Claude only | Accuracy on the same labeled examples | API latency and applicable rate limits | Actual model and token pricing and usage |
| Hybrid routing | Combined accuracy and share handled locally | Fallback rate, end-to-end latency, and throughput | Local operating cost plus hosted fallback usage |
If the workload is API-based and runs under load, check your organization’s current limits rather than assuming a universal quota. Anthropic documents limits in requests per minute, input tokens per minute, and output tokens per minute, with limits tied to organization tier. Anthropic’s rate-limit guidance, dated June 26, 2026, explains those dimensions.
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How to interpret the result
The useful outcome is not a single “GPU percentage.” It is a measured trade-off on your labeled workload: the local-handled share, the combined error profile, performance under expected load, and cost under clearly stated assumptions. Decide in advance what accuracy loss, if any, is acceptable; a hybrid system that routes more decisions locally may still be unsuitable if its errors affect important cases. Keep the local-only and Claude-only baselines so the hybrid result has context.
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