# FireOptimizer

Fireworks has added 3D FireOptimizer to its FireOptimizer tuning stack, a tool that automatically searches configurations spanning model architecture, quantization, speculation, hardware and parallelism. LLM serving must balance speed, throughput cost and quality, and because every knob affects the others the space exceeds 100,000 combinations. The tool prunes that space using production performance sweeps, heuristics and precomputed profiles, then takes a workload's characteristics and SLAs and returns a few candidate configurations; in three case studies, endpoints it produced gave a better QPS-versus-latency tradeoff than the public-platform baseline at the same quality bar.

Fireworks has added 3D FireOptimizer to its FireOptimizer tuning stack, a tool that automatically searches configurations spanning model architecture, quantization, speculation, hardware and parallelism. LLM serving must balance speed, throughput cost and quality, and because every knob affects the others the space exceeds 100,000 combinations. The tool prunes that space using production performance sweeps, heuristics and precomputed profiles, then takes a workload's characteristics and SLAs and returns a few candidate configurations; in three case studies, endpoints it produced gave a better QPS-versus-latency tradeoff than the public-platform baseline at the same quality bar.

Fireworks has added quantization-aware fine-tuning to its FireOptimizer adaptation engine, letting teams tailor the open DeepSeek R1 and V3 models to a specific use case. Previously, LoRA-tuned weights merged and served in FP8 lost noticeable accuracy, and the 671-billion-parameter base alone occupies 671 GB in FP8, above the typical 640 GB of an H100 host. The method fake-quantizes merged weights and activations in the forward pass so LoRA gradients stay in bfloat16 while merged weights follow FP8 numerics; on a French-wine review classification task with 15,000 training and 5,000 evaluation records, QAT tuning beat naive FP8 LoRA tuning.

Fireworks has added quantization-aware fine-tuning to its FireOptimizer adaptation engine, letting teams tailor the open DeepSeek R1 and V3 models to a specific use case. Previously, LoRA-tuned weights merged and served in FP8 lost noticeable accuracy, and the 671-billion-parameter base alone occupies 671 GB in FP8, above the typical 640 GB of an H100 host. The method fake-quantizes merged weights and activations in the forward pass so LoRA gradients stay in bfloat16 while merged weights follow FP8 numerics; on a French-wine review classification task with 15,000 training and 5,000 evaluation records, QAT tuning beat naive FP8 LoRA tuning.

Fireworks has added adaptive speculative execution to FireOptimizer, training the draft model for speculative decoding on a customer's own inference workload instead of generic conversation data. Speculative decoding lets a smaller draft model predict several tokens in parallel for the main model to verify, so the draft's hit rate sets the payoff; generic drafts hit poorly on specialized workloads. In one specialized case a generic draft hit 29% and raised latency 1.5x, while FireOptimizer reached a 76% hit rate and 2x speedup, up to 3x over a generic draft model.

Fireworks has added adaptive speculative execution to FireOptimizer, training the draft model for speculative decoding on a customer's own inference workload instead of generic conversation data. Speculative decoding lets a smaller draft model predict several tokens in parallel for the main model to verify, so the draft's hit rate sets the payoff; generic drafts hit poorly on specialized workloads. In one specialized case a generic draft hit 29% and raised latency 1.5x, while FireOptimizer reached a 76% hit rate and 2x speedup, up to 3x over a generic draft model.