Llama 3.1 8B Instruct Turbo is a throughput-optimized build of the standard 8B model. It keeps the full 131,072-token context window and tool calling support while favoring higher request capacity and efficient serving for systems handling large concurrent workloads. The optimization targets inference efficiency without changing the model's instruction-following or tool-use behavior. It suits high-traffic APIs and real-time services where both responsiveness and cost matter. It pairs well with batch inference and load-balancing strategies to amortize serving costs. The open-weight foundation permits self-hosting and fine-tuning when application-specific adjustments are needed alongside the speed-focused serving build.
Llama 3.1 8B Instruct Turbo runs on 1 route through the qynio gateway. Compare residency, zero retention, and training posture at a glance — full data-handling detail per route below.
| Provider | Region | Zero data retention | Training | Input | Output |
|---|---|---|---|---|---|
| US | Zero data retention | No | $0.02 | $0.04 |
Llama 3.1 8B Instruct Turbo runs on a single route through the qynio gateway today, so its availability is that provider's availability.
Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. .
Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider qynio monitors.
Each route hosting Llama 3.1 8B Instruct Turbo has its own privacy posture, residency, and no-KYC terms. Postures are maintained by qynio with a last-verification timestamp.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; unknown; deposit available.
Call Llama 3.1 8B Instruct Turbo through the qynio gateway with one API key. Let your coding agent set it up, or call it directly — qynio is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up qynio for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.QYN_API_KEY,baseURL: "https://api.qyn.io/v3/compat",});const completion = await client.chat.completions.create({model: "deepinfra/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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