Model benchmarks
google/gemma-4-e4b local LLM performance
As of September 2026, google/gemma-4-e4b runs at up to 19.9 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
4B
Peak speed
19.9 tok/s
Average speed
14.3 tok/s
Avg PP
278.9 tok/s
Min memory
2.0 GB
Max context
128.000 tokens
Avg output / run
9.778 tokens
Avg runtime / run
8m 41s
Avg quality
58.0
Benchmark runs
2
GPUs tested
1
Quality by task
Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 2 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 45.3 | 58.0 | 70.7 |
| Agent Workflow | 72.9 | 76.3 | 79.7 |
| Code Generation | 40.3 | 50.3 | 60.2 |
| Role Play & Narrative | 64.5 | 72.0 | 79.5 |
| Research & Analysis | 3.3 | 33.4 | 63.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination google/gemma-4-e4b has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| Advanced Micro Devices, Inc. [AMD/ATI] Phoenix1 | LM Studio | — | 19.9 tok/s | 19.9 tok/s | 5.9 GB | 32.768 tokens | 72.1 | 1 |
| CPU only | LM Studio | — | 8.7 tok/s | 8.7 tok/s | 2.0 GB | 128.000 tokens | 43.8 | 1 |
Benchmark runs
All 2 google/gemma-4-e4b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is google/gemma-4-e4b good for coding?
- In our benchmarks, google/gemma-4-e4b scores 50.3/100 for coding. It runs at about 14.3 tok/s, so if you want more speed, /home/polaris/models/Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL.gguf is faster (~35.6 tok/s) and still scores well for coding (84.9/100).
- Is google/gemma-4-e4b good for agentic (tool-using) tasks?
- In our benchmarks, google/gemma-4-e4b scores 76.3/100 for agentic workflows. It runs at about 14.3 tok/s, so if you want more speed, muse-glimmer:latest is faster (~34.5 tok/s) and still scores well for agentic workflows (93.0/100).
- How fast is google/gemma-4-e4b for local inference?
- Across 2 community benchmark runs, google/gemma-4-e4b reaches up to 19.9 tok/s and averages 14.3 tok/s, with the fastest results on Advanced Micro Devices, Inc. [AMD/ATI] Phoenix1.
- How much memory does google/gemma-4-e4b need?
- The leanest observed configuration used about 2.0 GB of memory.
- Which tools have been used to run google/gemma-4-e4b?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.