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Possibly—but the share you can handle locally depends on your model, data, and quality threshold. In one reported Banking77 experiment, 73.0% of 1,000 support-message decisions stayed on a fine-tuned local model running on an RTX 3080 Ti. That is a result to test against your own workload, not a prediction for it.
What the reported three-quarters result actually shows
Rob Hill of Fortitude Omnis Group reported that a fine-tuned local Laya decision model handled 73.0% of cases in a run of 1,000 Banking77 support messages, with uncertain cases passed to Claude Opus 5.5. The reported hybrid accuracy was 93.3%, compared with 94.2% for Claude alone. The author described the scope as “one dataset (Banking77), one card, one night.” Source: Rob Hill / Fortitude Omnis Group, indexed article excerpt (September 29, 2026)
The cost comparison was also an estimate, not a bill: £3,898 versus £1,054 per million decisions, calculated from published list prices. Further, the Claude answers came from an interactive Claude Code session working through batched answer sheets, not from the Claude API. It is therefore neither an API benchmark nor evidence of realized savings. The indexed excerpt does not establish enough detail about the local model version, prompt, tuning recipe, uncertainty threshold, or routing implementation to reproduce the result exactly. Treat the figures as a reported experiment, not an independently audited benchmark.
Measure local-only, Claude-only, and hybrid performance
A useful test does not ask only how many calls can stay on the GPU. It also checks whether the resulting classifications are good enough, how fast the system responds, and what it costs under a clearly defined accounting boundary.
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| 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 costs under stated assumptions |
| Claude only | Accuracy on the same labeled examples | API latency and applicable rate limits | Actual model and token pricing, plus measured usage |
| Hybrid routing | Combined accuracy and share handled locally | Fallback rate, end-to-end latency, and throughput | Local operating costs plus hosted fallback usage |
Build a fair test set and freeze the task
Start with the categories and inputs your real classifier must handle. Freeze the category list, prompt, output schema, and test inputs before comparing models. Use representative labeled examples that were not used to tune the local model; evaluating on training or tuning examples can make accuracy look better than it will be in use.
Keep the same examples and labeling standard for all three options. If inputs arrive in batches in production, preserve that behavior in the test. Record the model identifiers, local model configuration—including quantization where applicable—prompt, decoding settings, inference software version, and hardware. Claude’s model roster changes, so record the selected model ID and test date; Anthropic publishes model identifiers and capability information in its model overview.
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Run the three comparisons
Local model only
Run every test example through the local model without fallback. Measure accuracy against the labels and inspect the kinds of mistakes it makes, not just the aggregate score. A model can achieve a superficially acceptable total while failing on an important category.
Claude only
Run the same examples through the Claude option you are evaluating. If the intended deployment uses the API, use the API rather than an interactive coding session: those are different operating conditions. Save the model ID, request settings, and usage data so that quality and cost figures can be interpreted later.
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Hybrid routing
Apply a defined routing policy to the same examples: send cases judged uncertain by the local model to Claude, and leave the rest local. Count local-handled cases and Claude fallbacks, then score the final combined answers against the same labels. Report the local-handled share alongside the hybrid accuracy and error types; a high local share alone does not establish acceptable quality.
Measure speed, capacity, and cost under realistic conditions
Record end-to-end latency and throughput for each option, including the local inference time, fallback time, and batch behavior that the expected workload requires. A public project comparing llama.cpp, Ollama, and the Claude API tracks output speed, time to first token, and power; it is an example of useful measurement dimensions, not proof that its results apply to your hardware or workload. See its benchmark repository.
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Use the same accounting boundary when estimating cost. For hosted calls, use the model and token pricing that actually apply and compare estimates with usage records. Anthropic describes monitoring usage and cost by model and API key in its Claude platform documentation. For local inference, state relevant hardware, electricity, and amortization assumptions rather than treating an existing GPU as cost-free. The reported £3,898 and £1,054 figures above are list-price estimates per million decisions, not invoice-based measurements.
Under load, check the API limits that apply to your organization and tier. Anthropic describes limits in requests per minute, input tokens per minute, and output tokens per minute; current values are account-specific, so consult the rate-limit guidance instead of assuming a universal quota.
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Repeat the test and report uncertainty
Repeat the comparison on more than one representative data slice. Keep the evaluation examples separate from any tuning, and show where the models disagree or fail. Report the local share, accuracy, error patterns, latency, throughput, and cost assumptions together. That makes it possible to judge whether a routing policy is useful for your task rather than optimizing for the appealing but incomplete metric of how many calls stay local.
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