Fine-tune a 70B on one 80 GB card. What do NF4 and double quantization actually buy, and where does DoRA change the arithmetic?

LoRA shrinks the trainable state and leaves 141 GB of frozen weights untouched. QLoRA attacks the frozen half: NF4 takes it to 35 GB, double quantization compresses the scales themselves, paged optimizers absorb the spikes. The byte accounting, what dequantization costs, and where DoRA adds a term.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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🚀 Inference & Serving🔒 Premium

Multi-LoRA ServingA LoRA adapter is a few hundred megabytes of low-rank matrices that turn a base model into a fine-tuned variant, and multi-LoRA serving runs hundreds of them on one copy of the base weights by keeping the adapters in memory and applying the right one per request inside the batch. It is how a platform serves a thousand customers' fine-tunes without a thousand deployments. The costs are an extra small matmul per layer, adapter memory and loading, and a scheduler that has to batch across adapters without starving any of them.

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📐 AI Systems Design🔒 Premium

Multi-Tenant Fine-Tuning ServiceA fine-tuning service takes a customer's dataset and a base model and returns a model, and the design problem is that many customers want this at once, cheaply, without seeing each other's data, on GPUs that must not sit idle between jobs. LoRA changes the shape: an adapter is a few hundred megabytes rather than a copy of the base, so many jobs can share a base in memory and many adapters can be served from one replica. This page designs the service end to end: the pipeline, the LoRA arithmetic that sets memory and cost, the isolation, the scheduler that packs jobs, and the serving path.

Foundational

🧮 Open Weights & Serving Engines

Weight Formats: FP8 Blocks, MXFP4 and AWQOpen-weights models now ship pre-quantized, and the format is part of the release rather than something you choose afterwards. Block-scaled FP8 gives one byte per parameter with a scale per tile. MXFP4 gives about 0.53 bytes by pairing four-bit values with a shared exponent every 32 elements. Integer schemes like AWQ reach similar sizes with a different error profile. What decides a deployment is not which is most accurate in the abstract but which one the model was released and evaluated in, and which one your engine and hardware can execute natively.

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🚀 Inference & Serving🔒 Premium

Quantization for InferenceQuantization stores weights, and sometimes activations and the KV cache, in fewer bits, which cuts the bytes a decode step has to stream and the memory a model occupies. Weight-only int4 (GPTQ, AWQ) is a capacity and single-stream latency play; fp8 for weights and activations (W8A8) doubles tensor-core throughput and helps prefill and large batch; fp8 KV cache doubles context per GPU. Each has an accuracy cost you measure rather than assume, and knowing which one to reach for from the bottleneck is the interview question.

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UP NEXT ON YOUR JOURNEY

Next in this trackDesign the infrastructure for RLHF on a 70B: where do rollouts and the learner run, and how do weights move between them every step?Next in this trackYour team wants to replace a PPO-style RLHF pipeline with DPO. What leaves the cluster, and what does the training job look like afterwards?Next in this trackWe are running GRPO at scale. What does the infrastructure have to do that plain RLHF did not, and where do the GPUs sit idle?

FEDITOR'S NOTE

Scored on knowing that QLoRA quantizes the frozen base rather than the adapter, on the double-quantization arithmetic being about scale metadata, and on naming the dequantization cost rather than treating 4-bit as free.

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