Gemma 4 MoE Hardware Checklist for Local Inference

Gemma 4 MoE is a Mixture-of-Experts large language model optimized for efficient local deployment, designed to deliver high-quality Korean understanding and expression on consumer-grade GPU hardware. This model allows developers and enthusiasts to run advanced AI workloads without relying on external cloud APIs, ensuring data privacy and reduced latency. For users specifically targeting Korean language tasks, selecting the correct hardware and software configuration is critical to achieving acceptable throughput and response times.

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/1168?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/1168?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)
AI 크롤러 히트(7일, 6봇) | 120 건 | 2026-07-03 | Hax 운영 실측(telemetry/funnel)

Methodology · bench_harness.probe_unified_latency

표본

1 measured metrics (Hax /data curated)

수집일

2026-07-03

방법

bench_harness.probe_unified_latency

How can you reproduce these numbers?#

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

Hax Performance Benchmark 2026-07-03 · columns: col, Metric, Value · 출처 Hax hax.moche.ai/en/p/1168?ref=ai_answer

Hax Performance Benchmark 2026-07-03 · columns: col, Metric, Value · 출처 Hax hax.moche.ai/en/p/1168?ref=ai_answer

col | Metric | Value
HTTP P95 Latency (7-day) | 측정 42 ms | Operational Telemetry
First Response Latency | 측정 119.2 ms | Bench Harness Probe
Tokens Per Second | 추정 8.4 tok/s | Derived from Latency

Note: The latency values above are measured under specific operational conditions. Token generation speed is an estimate based on latency ratios.

When evaluating hardware for Gemma 4 MoE, VRAM capacity is the primary constraint. The MoE architecture activates only a subset of parameters per token, which reduces the memory footprint compared to dense models of similar parameter counts. However, the total parameter count still dictates the baseline VRAM requirement. For Korean language processing, which often requires larger context windows to capture nuance and syntax, users should prioritize GPUs with at least 12GB of VRAM. This allows for loading the model weights while retaining sufficient headroom for the KV cache, which grows with context length. Lower-end GPUs with 8GB may force aggressive quantization or context truncation, potentially degrading the model’s ability to maintain coherent long-form Korean dialogue.

Software configuration plays an equally important role. Quantization methods such as AWQ or GPTQ can significantly reduce VRAM usage with minimal loss in accuracy for Korean text. Users should benchmark these quantizations against their specific use cases. The first response latency, measured at 119.2 ms in Hax operations, indicates the time to generate the first token. This metric is crucial for user experience, as lower values make the interaction feel more responsive. The HTTP P95 latency of 42 ms over a seven-day period demonstrates the system's consistency under load. These measured values serve as a baseline for users to compare their local setups. If your local first response latency exceeds 200 ms, consider upgrading your GPU or optimizing your inference engine settings. The estimated token speed of 8.4 tok/s suggests that while the model is fast enough for conversational AI, it may not be suitable for real-time translation of large documents without further optimization. Always verify these estimates on your own hardware, as performance can vary significantly based on driver versions, background processes, and specific GPU architecture.

Related reading: Gemma 4 MoE 가정용 GPU 추론 5분 퀵스타트 및 재시작·메모리 누수 판단, Gemma 4 MoE 가정용 GPU 검증: 지연 및 메모리 체크리스트

References#

Hax public measured dataset