quantization

Quantize a sixteen-value block, count the scale bytes, and use paired quality tests to decide which tensors need higher precision.

Derive when operand scales can be applied after a dot product and when they must follow its K blocks. Separate integer zero points, floating-point range, scale granularity and accumulator behavior.

Separate packed base weights, quantization metadata and trainable adapter state. Count the real projection shapes, then leave room for activations and temporary work before calling a one-card configuration feasible.

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

Compare actual encoded tensors, per-rank runtime fit, supported kernels and held-out task quality. Four-bit weight-only kernels can accelerate wider arithmetic, and smaller weights need not reduce the GPU count.

Confirm that requests are waiting for usable state capacity. Separate allocated memory, live blocks and evictable prefixes, then compare admission, lower-precision state, prefix sharing and hardware changes with explicit assumptions.

Define whether half the footprint means checkpoint bytes or runtime memory. Choose a supported recipe, measure which tensors it changes, separate calibration from held-out evaluation, and validate quality and serving cost.

Validate the exact model, accelerator generation and serving features. Separate packed storage from kernel execution, derive the real memory fallback, and compare qualified throughput and total migration cost.

Follow a shard index to the tensors a loader actually receives. Diagnose a missing file and a shape mismatch, then check architecture, quantization and request compatibility before changing hardware.

Separate tensor format, model semantics and runtime support. Budget weights plus context state, and check conversion before judging a quantized artifact.

Map a checkpoint format to compute capability, then say which part of the speedup survives on each generation. The same file can load on two GPUs and take two different execution paths.

Compare raw weight sizes at 16, 8 and 4 bits, then budget KV cache and runtime memory for Llama 3.1 70B. Follow the worked per-GPU example.

Calculate FP8 savings by separating weights, KV cache and other step costs. Compare the same batch first, then check whether larger batches meet latency and quality targets.

Derive the scale, handle zero tensors, clamp before casting, and separate rounding error from clipping. Compare one outlier under tensor and channel scales.