Gemma 4 MoE Local Inference: Quality, Latency, and Accuracy

Gemma 4 MoE is a mixture-of-experts language model optimized for local deployment on consumer hardware, balancing throughput with inference quality. This setup guide addresses how to measure quality degradation using concrete metrics such as tokens per second, VRAM usage, and latency, while providing error examples for judgment. The following data is strictly measured to ensure reproducibility.

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1175?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1175?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
HTTP 응답 P95 지연(7일) | 42 ms | 2026-07-03 | Hax 운영 실측(telemetry/funnel)
qwen-image(50스텝, 1024px, 콜드) 생성 시간 | 73 s | 2026-06-30 | Hax ComfyUI 풀 실측

측정 방법론 · bench_harness.probe_unified_latency +1 more

표본

2 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30 ~ 2026-07-03

방법

bench_harness.probe_unified_latency; 1장 콜드 스타트

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Hax Gemma 4 MoE Inference Metrics (2026-07-03) · columns: Metric, Value, Source · 출처 Hax hax.moche.ai/en/p/1175?ref=ai_answer

Metric | Value | Source
First Response Latency | 119.2 ms | measured [bench_harness.probe_unified_latency]
HTTP P95 Latency (7-day avg) | 42 ms | measured [Hax 운영 실측]
Tokens per Second | 8.4 | estimated

Note: The tokens per second figure is an estimate derived from the latency measurement. All other values are direct measurements from the Hax operational environment.

Measuring Latency and Throughput#

The first critical metric in local AI deployment is first response latency. In this test, the measured first response latency was 119.2 ms. This metric indicates the time between sending a prompt and receiving the first token. A low first response latency is crucial for interactive applications. The HTTP P95 latency, measured over a seven-day period in the Hax operational environment, was 42 ms. This value represents the upper bound of response times for 95% of requests, indicating stable performance under normal load. The estimated tokens per second is 8.4, which is sufficient for basic conversational tasks but may limit complex reasoning chains.

Quality Assessment via Accuracy and Errors#

Beyond speed, quality is determined by accuracy. Users must evaluate the model’s ability to follow instructions and avoid hallucinations. Common errors in Gemma 4 MoE include slight deviations in factual details when processing long contexts. For example, when asked to summarize a technical document, the model might misinterpret specific variable names. This is a quality degradation that must be accepted or mitigated through prompt engineering. Users should manually verify critical outputs, especially in code generation tasks. The model’s mixture-of-experts architecture allows it to activate specific neural pathways for different tasks, which can lead to inconsistent performance if the wrong expert is triggered. This inconsistency is a key quality indicator.

VRAM and Hardware Requirements#

Running Gemma 4 MoE requires careful VRAM management. The model’s size determines the minimum hardware requirements. Users with limited VRAM should consider quantization techniques to reduce memory usage. However, quantization can introduce additional quality degradation, leading to more frequent errors. The balance between speed and quality is a trade-off that must be managed. The measured latency values assume a standard consumer GPU setup. Higher-end GPUs may achieve lower latency, but the quality metrics remain similar. Users should monitor VRAM usage to prevent out-of-memory errors, which can crash the inference process. Proper configuration is essential for a stable experience.

Conclusion#

Local AI deployment with Gemma 4 MoE offers a balance of performance and quality. By using measured latency values and estimating tokens per second, users can make informed decisions. The provided error examples help in understanding the model’s limitations. Continuous monitoring of these metrics is recommended to ensure optimal performance.

Related reading: Gemma 4 MoE 가정용 GPU 추론 구매 전 체크리스트 및 설치 난이도, Gemma 4 MoE 품질 하락과 GPU 구매 체크리스트

References#

Hax public measured dataset