LLaMA 3 8B vs Gemma 2 9B for API Serving (Throughput): GPU Benchmark

Quick Verdict

LLaMA 3 8B handles 22.5 requests per second. Gemma 2 9B manages 17.2. That 31% throughput gap is not a rounding error — it is the difference between a single GPU handling your API traffic and needing to provision a second server. But look at the tail latency: LLaMA 3 8B’s p99 of 235 ms versus Gemma 2 9B’s 409 ms tells an even starker story. Under load, Gemma 2 9B’s safety-oriented inference pipeline introduces latency spikes that are hard to mask behind a load balancer. On a dedicated GPU server, this makes LLaMA 3 8B the clear default for API workloads with SLAs.

For broader model comparisons, see our GPU comparisons hub.

Specs Comparison

API serving rewards two things above all else: small memory footprint (more room for batched requests) and architectural simplicity (fewer compute bottlenecks under concurrent load). Both models are dense transformers, but their VRAM profiles differ enough to affect real-world batching on self-hosted infrastructure.

LLaMA 3 8B’s 0.5 GB VRAM advantage at INT4 directly translates into room for more concurrent KV caches under vLLM. For detailed breakdowns, see our guides on LLaMA 3 8B VRAM requirements and Gemma 2 9B VRAM requirements.

API Throughput Benchmark

We tested both models on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with INT4 quantisation, continuous batching, and a sustained concurrent request load. This simulates a production API endpoint under real traffic conditions. For live speed data, check our tokens-per-second benchmark.

The counterintuitive detail: Gemma 2 9B has a lower p50 latency (69 ms vs 88 ms), meaning median requests are actually faster. The problem is the p99 — under peak load, Gemma 2 9B’s tail latency balloons to 409 ms, nearly double LLaMA 3 8B’s. For API endpoints with latency SLAs, the p99 is the number that matters. Visit our best GPU for LLM inference guide for hardware-level comparisons.

See also: LLaMA 3 8B vs Gemma 2 9B for Chatbot / Conversational AI for a related comparison.

See also: LLaMA 3 8B vs DeepSeek 7B for API Serving (Throughput) for a related comparison.

Cost Analysis

API serving economics are dominated by utilisation rate. A model that handles more requests per second on the same dedicated GPU server costs less per request at every traffic level.

At 22.5 req/s versus 17.2, LLaMA 3 8B serves 31% more requests per hour. Over a month, that gap can mean the difference between one server and two. Use our cost-per-million-tokens calculator to run the exact numbers for your traffic volume.

Recommendation

Choose LLaMA 3 8B for any API endpoint with latency SLAs or high concurrent traffic. The 22.5 req/s throughput and predictable 235 ms p99 make it the safer choice for production deployments where tail latency violations trigger alerts or degrade user experience.

Choose Gemma 2 9B if your API traffic is moderate and you value response quality over throughput. Gemma 2 9B’s lower p50 latency means most individual requests feel fast — the p99 penalty only surfaces under sustained high concurrency. For internal APIs with controlled traffic patterns, Gemma 2 9B’s quality advantages may outweigh its throughput limitations.

Serve either model behind vLLM on a dedicated GPU server with continuous batching for optimal throughput per pound spent.