Gemma 4 MoE Home GPU Checklist: p50/p95 Latency Analysis
In short: Gemma 4 MoE is a mixture-of-experts language model designed for efficient local inference on consumer hardware. It represents a shift from dense model architectures to sparse activation, allowing users to achieve higher performance with significantly lower video memory requirements compared to traditional dense models of similar parameter counts.
Gemma 4 MoE is a mixture-of-experts language model designed for efficient local inference on consumer hardware. It represents a shift from dense model architectures to sparse activation, allowing users to achieve higher performance with significantly lower video memory requirements compared to traditional dense models of similar parameter counts.
| col | Model Metric | Hax Measured Value | Source |
|---|---|---|---|
| row | First Response Latency | 119.2 ms | bench_harness.probe_unified_latency |
| row | HTTP P95 Latency | 42 ms | Hax 운영 실측 (7-day avg) |
| row | Tokens Per Second | 8.4 tok/s | Estimated (derived) |
- 표본
- 1 measured metrics (Hax /data curated)
- 수집일
- 2026-07-03
- 방법
- bench_harness.probe_unified_latency
The first response latency of 119.2 ms was measured on 2026-07-03 using the bench_harness.probe_unified_latency tool. This metric represents the time from prompt submission to the first token generation. In contrast, the HTTP response P95 latency was measured at 42 ms over a seven-day period via Hax operational telemetry. This discrepancy highlights the difference between initial cold-start overhead and sustained throughput stability.
What hardware specifications are required?#
The estimated VRAM requirement depends on the active expert count. For Gemma 4 MoE, users should prioritize GPUs with at least 12GB of VRAM for 7B-class equivalents, though 16GB is recommended for comfortable batching. The token per second estimate of 8.4 is derived from the measured latency values. This estimation assumes a quantized 4-bit implementation on mid-range consumer hardware.
How does MoE affect inference latency?#
Mixture-of-Experts models activate only a subset of parameters per token. This reduces the computational load, directly impacting the p50 and p95 latency metrics. The measured HTTP P95 latency of 42 ms indicates high consistency in network delivery, whereas the first response latency includes model warmup and context processing overhead.
Note: Hardware performance varies by driver version and OS optimization. The estimated 8.4 tokens per second is a derived value based on the measured latency of 119.2 ms for a single token response under specific benchmark conditions.
Related reading: 음성 클로닝 오픈모델, 흔한 함정과 해결법, 음성 클로닝 오픈모델, 2026 현황과 추천
References#
- Google DeepMind Gemma Documentation
- Hax Bench Harness Latency Metrics
- Mixture of Experts Architecture Overview
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