Run AI models entirely offline. Benchmark inference performance. Compare quality vs speed tradeoffs on real hardware. No cloud. No API keys. No data leaves your machine.
Three small language models running entirely on local hardware via Ollama. No GPU required.
Same prompts, same hardware, same conditions. Pure head-to-head comparison across five diverse tasks.
Run a benchmark to see results
Run a benchmark to see results
Run a benchmark to see results
Run a benchmark to see results
Why run models locally? Because privacy, latency, and cost constraints are real.
With local models, zero data leaves your infrastructure. No third-party API sees your prompts, responses, or training data. Critical for healthcare, legal, finance, and any organization under GDPR, HIPAA, or SOC 2 requirements. Cloud APIs require trust in a vendor's data handling — local inference requires trust only in yourself.
Cloud API latency includes network round-trip, queue wait, and rate-limit backoff. Local inference has predictable, consistent latency — no cold starts, no 429s, no outage dependencies. On CPU-only hardware, smaller models trade output quality for sub-second response times. The right model depends on your SLA.
Cloud APIs charge per token. At high volume, costs grow linearly with usage. Local inference has a fixed infrastructure cost — the same VPS processes 1,000 or 100,000 requests at identical cost. Break-even typically hits at ~10K requests/day for small models on modest hardware.
Smaller models (1.5B-3.8B params) are quantized to 4-bit, trading precision for memory efficiency. They handle focused tasks well — extraction, classification, summarization — but struggle with nuanced reasoning, creative writing, and multi-step logic compared to 70B+ cloud models. Match the model to the task.
| Factor | Local SLM (This Setup) | Cloud API (GPT-4, Claude, etc.) |
|---|---|---|
| Privacy | Complete — no data leaves server | Vendor-dependent, requires DPA |
| Latency (TTFT) | 50-200ms (no network hop) | 200-2000ms (network + queue) |
| Throughput | Limited by hardware (5-20 tok/s CPU) | High, scales with spend |
| Output Quality | Good for focused tasks | Excellent across all tasks |
| Cost (10K req/day) | ~$20/mo (VPS fixed cost) | ~$300-1500/mo (per-token) |
| Availability | 100% uptime (your infra) | 99.9% SLA, outage risk |
| Customization | Fine-tune freely | Limited to provider offerings |
Send a prompt to any loaded model. Everything runs on this server — your data never leaves.