Fireworks AI LLM Inference & Serving interview questions

Follow a token into retained attention state, derive bytes per position for a 70B model, and distinguish present cache usage from the room needed to finish a response.

Trace an accepted prefix, a rejection and the correction token; derive emitted progress and compare it with a measured round cost.

Draw query-head sharing, count actual stored state and compare payloads with explicit model geometry and binary units.

Separate weight storage, matrix arithmetic and attention-cache precision. Choose a supported candidate using task quality, memory and service measurements.

Compare a complete phase handoff with a tuned combined pool, then test the queueing reversal and qualified completions. Nominal link speed and fleet size alone cannot decide the split.

Follow adapter-specific token rows through a shared base, calculate stored adapter bytes, and separate the resident set from the active batch.

Separate checkpoint memory, expert assignments and retained MLA state. The active parameter count alone cannot predict per-rank fit or token latency.

Calculate retained cache and prefill work for a stated 128k model shape, then choose scheduling and memory policies from measured limits.

Compare proposal methods by committed output over complete round time, with explicit sampling guarantees and target compatibility.

Build a measured first-token budget and test the smallest serving design that meets it, including cold prompts, queueing and delivery.

Images add preprocessing and encoder work before the language model can answer. Their effect on prompt length and cache memory depends on the model. Budget each stage, and distinguish image-embedding reuse from prefix-cache reuse.

Diagnose a stream that begins quickly but produces useful answer content late. Compare generation, parser and client clocks, then test a reasoning budget without hiding truncated or incorrect responses.

Follow the weight bytes through a small decode batch and a long prefill. A scoped roofline calculation explains the difference without pretending to predict actual latency.

Work from each GPU's cache budget, reserve room for generated tokens, then check latency separately. A limiting-rank example shows why fitting today's prompts is not enough to admit tomorrow's work.

Work from token timestamps to first-token wait, average pace and individual stalls. Then count requests meeting both latency limits, keeping failures and measurement boundaries explicit.

Trace one cache position through a block table, calculate the memory recovered from oversized reservations, and explain why sharing still needs careful writes and matching.

One request finishes, another keeps writing, and a third waits outside. Follow the same workload through two schedules to see what continuous batching improves, and what it cannot promise.

Trace a 20,000-token prompt through a shared token budget, including the last partial chunk. Then choose a setting from measured first-token waits and streaming gaps.