Together AI LLM Inference & Serving interview questions

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.

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

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.

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

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

Filter engine candidates by exact compatibility, then compare correctness, goodput and operational cost on the same workload.

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

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

Separate device throughput bounds from measured service curves, then choose a feasible operating point using latency, quality and goodput.

Translate request demand into prompt work, generated tokens and retained state, then size a measured serving configuration and its failure reserve.

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.

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.

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

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.

Trace a model startup through storage, runtime setup and device loading. Transfer floors and dependencies determine whether a minute is plausible.

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