Faster is only better if the output remains acceptable.
A faster kitchen fails if dishes leave raw. Keep a tasting panel and fixed orders beside the stopwatch; performance and quality are two separate acceptance gates.
Use representative prompts and task metrics: exact match, pass@k, retrieval quality, factuality review, schema validity, preference judgments, or domain-specific checks. Perplexity (perplexity = a generic score of how well a model predicts held-out text; lower is better) alone is rarely the product metric.
For deterministic comparisons use greedy decoding or fixed settings and seeds where supported. For sampling products compare distributions or repeated trials; one anecdotal output proves little.
Caching and mathematically exact speculative decoding should preserve the target distribution (the distribution = the model's probability spread over the next token; see Lesson 8) when implemented correctly. Quantization, changed kernels, altered sampling, or changed chat templates may move quality and require evaluation.
First require quality within an agreed tolerance, then compare SLO-valid performance on the same workload. Record model/engine revisions and investigate slice regressions, not only the aggregate.
Latency and error metrics cannot detect a fluent but worse model. Production rollout needs offline gates plus sampled online quality signals and a rollback threshold.
Your cluster serves FP8 weights and FP8 KV cache, and the DFlash experiment compared speculative and baseline serving. Those are performance results; shipping either change still requires a workload-specific quality gate.
Faster is only better if the output remains acceptable.
A faster kitchen fails if dishes leave raw. Keep a tasting panel and fixed orders beside the stopwatch; performance and quality are two separate acceptance gates.
| Define quality for the task | Use representative prompts and task metrics: exact match, pass@k, retrieval quality, factuality review, schema validity, preference judgments, or domain-specific checks |
| Control randomness | For deterministic comparisons use greedy decoding or fixed settings and seeds where supported |
| Separate lossless and lossy changes | Caching and mathematically exact speculative decoding should preserve the target distribution (the distribution = the model's probability spread over the next token; see Lesson 8) when implemented correctly |
| Use paired gates | First require quality within an agreed tolerance, then compare SLO-valid performance on the same workload |
Use representative prompts and task metrics: exact match, pass@k, retrieval quality, factuality review, schema validity, preference judgments, or domain-specific checks. Perplexity (perplexity = a generic score of how well a model predicts held-out text; lower is better) alone is rarely the product metric.
For deterministic comparisons use greedy decoding or fixed settings and seeds where supported. For sampling products compare distributions or repeated trials; one anecdotal output proves little.
Caching and mathematically exact speculative decoding should preserve the target distribution when implemented correctly. Quantization, changed kernels, altered sampling, or changed chat templates may move quality and require evaluation.
First require quality within an agreed tolerance, then compare SLO-valid performance on the same workload. Record model/engine revisions and investigate slice regressions, not only the aggregate.
Latency and error metrics cannot detect a fluent but worse model. Production rollout needs offline gates plus sampled online quality signals and a rollback threshold.
Your cluster serves FP8 weights and FP8 KV cache, and the DFlash experiment compared speculative and baseline serving. Those are performance results; shipping either change still requires a workload-specific quality gate.