/**
 * @typedef {object} DeviceProbe
 * @property {boolean} webgpu
 * @property {string} [reason] why WebGPU is unusable, when it is
 * @property {object} [adapter] vendor / architecture / device, where exposed
 * @property {{shaderF16: boolean}} [features]
 * @property {object} [limits]
 * @property {boolean} [kvReuse] whether cross-turn KV reuse can be used here
 * @property {{quota?: number, usage?: number, persisted?: boolean}} storage
 * @property {number} [deviceMemoryGB] Chrome only; absent is not "small"
 */
/**
 * Reads what this machine will admit to. Never throws: an unusable device is a
 * result, not an error — the caller's job is to explain it, not to crash.
 *
 * @returns {Promise<DeviceProbe>}
 */
export function probeDevice(): Promise<DeviceProbe>;
/**
 * Whether a model can run on a probed device.
 *
 * `blockers` mean it will not work; `warnings` mean it will work worse, or
 * might not fit. The split matters: a caller should refuse to start on a
 * blocker and merely inform on a warning, and conflating the two is how you end
 * up either crashing or refusing to run something that would have been fine.
 *
 * @param {{model_id: string, vram_required_MB?: number, sizeBytes?: number}} model
 * @param {DeviceProbe} probe
 * @returns {{ok: boolean, blockers: Array<{code: string, message: string}>,
 *   warnings: Array<{code: string, message: string}>}}
 */
export function canRun(model: {
    model_id: string;
    vram_required_MB?: number;
    sizeBytes?: number;
}, probe: DeviceProbe): {
    ok: boolean;
    blockers: Array<{
        code: string;
        message: string;
    }>;
    warnings: Array<{
        code: string;
        message: string;
    }>;
};
/**
 * Rank a model list by what this device can actually run.
 *
 * The prebuilt list spans 239 MB to 31 GB, so "which model should I use" is the
 * first question a developer has and the one they have least basis to answer.
 * Runnable models come first, then fewest warnings; unrunnable ones are kept at
 * the end carrying their reason rather than silently dropped, because "why
 * can't I use that one" is the next question.
 *
 * **`prefer` is a real choice, not a default worth hiding.** Decode here is
 * memory-bandwidth-bound — time per token scales with weight bytes (AI.md,
 * "Why not llama.cpp/Ollama-class"), so the largest model that fits is also the
 * slowest thing that fits. `"quality"` picks the biggest, `"speed"` the
 * smallest. Neither is right for everyone, which is why it is a parameter.
 *
 * Vision models are excluded from a text ranking rather than merely deprioritised:
 * a VLM answers text prompts perfectly well, but at several times the download
 * for no benefit, so recommending one to a caller who did not ask is bad advice.
 *
 * @param {Array<object>} models `model_list` entries or registry records
 * @param {{probe: DeviceProbe, maxVramMB?: number, needsVision?: boolean,
 *   prefer?: "quality" | "speed"}} opts
 */
export function rankModels(models: Array<object>, { probe, maxVramMB, needsVision, prefer }?: {
    probe: DeviceProbe;
    maxVramMB?: number;
    needsVision?: boolean;
    prefer?: "quality" | "speed";
}): {
    ok: boolean;
    blockers: Array<{
        code: string;
        message: string;
    }>;
    warnings: Array<{
        code: string;
        message: string;
    }>;
    model: any;
}[];
/**
 * @param {number} modelBytes
 * @param {number} [bytesPerSecond] this machine's measured rate, when known
 * @returns {{tokensPerSecond: number, basis: "measured" | "extrapolated",
 *   modelBytes: number, bytesPerSecond: number, reference?: string}}
 */
export function projectSpeed(modelBytes: number, bytesPerSecond?: number): {
    tokensPerSecond: number;
    basis: "measured" | "extrapolated";
    modelBytes: number;
    bytesPerSecond: number;
    reference?: string;
};
/**
 * Decode throughput is memory bandwidth divided by weight bytes.
 *
 * This project measured the whole chain: decode reaches ~16 GB/s of the M4's
 * ~120 GB/s, and the 1.06 GB build runs 16.6–18.1 tok/s — which is that
 * quotient. So a projection needs one number, the *achieved* bandwidth, and
 * everything else follows from model size.
 *
 * The constant below is that machine's figure and is only a starting point. The
 * moment this engine has decoded anything it knows the real number for the
 * machine it is on, and `ScheduledEngine.estimateSpeed()` switches to it — so
 * this is a cold-start default, not a claim about anyone's hardware.
 */
export const REFERENCE_DECODE_BYTES_PER_SECOND: 17000000000;
export const REFERENCE_DEVICE: "M4 MacBook Air (16 GB), Firefox";
export type DeviceProbe = {
    webgpu: boolean;
    /**
     * why WebGPU is unusable, when it is
     */
    reason?: string;
    /**
     * vendor / architecture / device, where exposed
     */
    adapter?: object;
    features?: {
        shaderF16: boolean;
    };
    limits?: object;
    /**
     * whether cross-turn KV reuse can be used here
     */
    kvReuse?: boolean;
    storage: {
        quota?: number;
        usage?: number;
        persisted?: boolean;
    };
    /**
     * Chrome only; absent is not "small"
     */
    deviceMemoryGB?: number;
};