Gemma 4 GPU/CPU Offload Diagnosis Guide
A practical troubleshooting guide for Gemma 4 when performance feels CPU-bound despite high reported GPU usage.
April 6, 20262 min read
Gemma 4
Offload
GPU
CPU
Performance
If Gemma 4 feels slow while your dashboard says GPU usage is high, you are likely dealing with an offload or memory-pressure pattern.
This guide focuses on diagnosis, not guesswork.
Symptom Pattern
Typical report:
- GPU appears active
- latency remains high
- token speed is unstable
- longer prompts worsen performance dramatically
This usually means your runtime is not in the execution mode you assume.
Common Root Causes
- Context size too high for available memory headroom
- Partial CPU offload due to memory pressure
- Runtime buffer contention under concurrency
- Misleading utilization metrics (high load, low effective throughput)
First 5 Checks
- Reduce context by 50% and retest speed stability.
- Run single-session test (no parallel requests).
- Compare first-token latency vs steady tokens/sec.
- Confirm model + quantization variant is what you intended.
- Reproduce in one alternate runtime to isolate stack behavior.
If performance normalizes after lowering context, KV cache pressure is likely your main bottleneck.
Measurement Priorities
Do not rely on a single GPU % metric.
Track:
- first-token latency
- average tokens/sec over a 3-5 minute window
- p95 latency for your real prompt mix
- error/retry count under load
These reflect user-facing performance better than utilization screenshots.
Fast Mitigation Sequence
Use this order:
- Lower context
- Lower concurrency
- Use a lighter quantization profile
- Re-test with fixed prompts
- Only then attempt advanced tuning
Most teams skip step 1 and waste time on micro-optimizations.
Decision Table
| Observation | Likely cause | Immediate action |
|---|---|---|
| Good short-prompt speed, bad long-prompt speed | KV cache growth | Lower context target |
| Fast at cold start, slows over session | memory pressure accumulation | reset/session policies + lower concurrency |
| Similar slowness across runtimes | hardware budget mismatch | choose smaller model/quant |
| Only one runtime is slow | runtime-specific config/path | version pin + config diff |
Final Takeaway
For Gemma 4, offload issues are usually solved by better memory budgeting, not by exotic tuning.
If you diagnose with stable metrics and fixed prompt sets, the root cause becomes obvious quickly.