Optimizing LLM Inference: Gemma 4 26B on RTX 5090

Efficiency in Large Language Model (LLM) inference represents a critical challenge for organizations aiming to implement AI solutions in self-hosted environments. A recent benchmark has highlighted the significant progress achievable through advanced optimization techniques, demonstrating how the Gemma 4 26B model, quantized to 4-bit AWQ, can achieve high performance on a single high-end consumer GPU.

Specifically, tests conducted with the vLLM framework, version 0.19.2rc1, explored the impact of DFlash speculative decoding. This technique aims to improve throughput and reduce latency by generating a draft output in advance with a smaller, faster model, and then validating it with the main model. The results obtained on an NVIDIA RTX 5090, equipped with 32GB of VRAM, offer significant insights for those evaluating LLM deployment in on-premise contexts.

Technical Details of the Benchmark

The benchmark setup utilized a main model, cyankiwi/gemma-4-26B-A4B-it-AWQ-4bit, alongside a draft model, z-lab/gemma-4-26B-A4B-it-DFlash, for speculative decoding. The workload consisted of requests with 256 input tokens and 1024 output tokens, processed with unitary concurrency and request rate on a random dataset.

Without the aid of DFlash speculative decoding, the system recorded a throughput of approximately 228 output tokens per second, with an average end-to-end latency of about 4455 milliseconds. The introduction of DFlash, optimized with 13 speculative tokens and a max_num_batched_tokens of 8192, led to a substantial increase in performance. Throughput rose to approximately 578 output tokens per second, while average latency dropped to about 1738 milliseconds, representing an acceleration of approximately 2.56 times compared to the baseline. It is interesting to note how optimizing max_num_batched_tokens to 8192 contributed to improving latency stability, particularly for the 95th percentile (p95), compared to configurations with smaller batch sizes.

Implications for On-Premise Deployments

These results are particularly relevant for companies considering LLM deployment in on-premise or hybrid environments. The ability to achieve high performance on a single consumer-grade GPU, such as the RTX 5090, suggests that it is possible to implement complex models like Gemma 4 26B without necessarily resorting to expensive cloud infrastructures or enterprise-level GPU clusters for specific workloads.

Optimization through techniques like DFlash speculative decoding and efficient VRAM management (32GB in this case) are key factors in containing the Total Cost of Ownership (TCO) and ensuring data sovereignty. Running inference locally allows for complete control over sensitive data and adherence to stringent compliance requirements, fundamental aspects for sectors such as finance or healthcare. For those evaluating the trade-offs between self-hosted and cloud solutions, AI-RADAR offers analytical frameworks on /llm-onpremise to delve deeper into these dynamics.

Future Prospects and Trade-offs

The continuous evolution of inference frameworks and optimization techniques, such as quantization and speculative decoding, is democratizing access to increasingly powerful LLMs. While the results of this benchmark are promising, it is crucial to consider that deployment needs vary widely. Factors such as scalability, managing multi-user workloads, and the diversity of models to be served require an in-depth analysis of the infrastructure.

The choice between different hardware and software configurations always involves trade-offs between performance, cost, and operational complexity. The goal is to find the right balance that meets the specific requirements of each scenario, maximizing efficiency without compromising stability or security. These benchmarks help provide concrete data to inform such decisions, highlighting the value of innovation in the field of LLM inference.