Gemma 4 MoE Home GPU: Latency, VRAM, and Leak Analysis

Gemma 4 MoE is a mixture-of-experts large language model designed to balance high performance with efficient local deployment on consumer-grade hardware. This architecture determines whether a home GPU stack requires an upgrade by analyzing token throughput, VRAM consumption, and system stability over 24-hour continuous inference cycles. For operators managing local AI services, the decision to upgrade hardware depends on measurable latency thresholds and the presence of memory leaks that necessitate frequent service restarts.

Hax Operations / 2026-07-03 Bench Harness · columns: col, Metric, Value · 출처 Hax hax.moche.ai/en/p/1135?ref=ai_answer

col | Metric | Value
first_response_latency | measured | 119.2 ms
http_p95_latency_7d | measured | 42 ms
tokens_per_second | estimated | 8.4

Methodology · bench_harness.probe_unified_latency

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1 measured metrics (Hax /data curated)

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2026-07-03

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bench_harness.probe_unified_latency

The measured HTTP P95 latency of 42 ms over a seven-day period indicates a stable serving environment. In contrast, the first-response latency of 119.2 ms reflects the initial computational cost of activating the MoE routing logic. These measured values provide a baseline for evaluating hardware sufficiency. If your setup exceeds 200 ms for first responses under similar load, an upgrade to higher-bandwidth VRAM may be necessary. The estimated throughput of 8.4 tokens per second suggests that mid-range GPUs can handle conversational workloads but may struggle with long-context generation without quantization.

How to determine if VRAM is the bottleneck?#

Visual inspection of VRAM usage is the first step. If the model fits within available memory but performance degrades over time, the issue is likely thermal throttling or memory fragmentation. Gemma 4 MoE utilizes sparse activation, meaning not all parameters are loaded simultaneously. However, the routing tokens still consume bandwidth. An estimated 8.4 tok/s throughput is acceptable for interactive use but insufficient for high-concurrency APIs. Operators should monitor the VRAM utilization curve. A steady climb without release indicates a leak.

When to restart to fix memory leaks?#

Memory leaks in local inference engines often manifest as gradual VRAM exhaustion. If the system requires a restart every 12 hours to maintain stability, this is a critical failure. The measured data from Hax operations shows stable latency, implying no significant leaks in the tested configuration. However, community deployments often suffer from driver-level leaks. A restart is necessary when free VRAM drops below 500 MB or when token generation speed decreases by more than 20% over a four-hour window. This degradation is an estimated indicator of heap fragmentation.

Note: The estimated throughput of 8.4 tok/s varies based on quantization format and context length.

Related reading: 유료 모델 1/30 값에 코딩 실력이 비등한 오픈웨이트 AI, DeepSeek V4는 어디까지 왔나?, 음성 클로닝 오픈모델, 흔한 함정과 해결법

References#

Google Gemma Model Documentation

Hax Operations Telemetry Overview

LLM Inference Best Practices