Model benchmarks
gemma-4-26b-a4b local LLM performance
As of October 2026, gemma-4-26b-a4b runs at up to 117.4 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
26B
Peak speed
117.4 tok/s
Average speed
79.6 tok/s
Avg PP
641.2 tok/s
Min memory
16.7 GB
Max context
32,768 tokens
Avg output / run
8,891 tokens
Avg runtime / run
2m 1s
Avg quality
70.1
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 | 69.1 | 70.1 | 71.0 |
| Agent Workflow | 70.5 | 71.6 | 72.6 |
| Code Generation | 60.5 | 66.2 | 71.8 |
| Role Play & Narrative | 70.1 | 73.6 | 77.2 |
| Research & Analysis | 68.4 | 68.9 | 69.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination gemma-4-26b-a4b has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5080 | llama.cpp | — | 117.4 tok/s | 79.6 tok/s | 16.7 GB | 32,768 tokens | 70.1 | 2 |
Benchmark runs
All 2 gemma-4-26b-a4b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is gemma-4-26b-a4b good for coding?
- In our benchmarks, gemma-4-26b-a4b scores 66.2/100 for coding. It runs at about 79.6 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MLX-oQ4e is faster (~107.9 tok/s) and still scores well for coding (81.5/100).
- Is gemma-4-26b-a4b good for agentic (tool-using) tasks?
- In our benchmarks, gemma-4-26b-a4b scores 71.6/100 for agentic workflows. It runs at about 79.6 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~116.8 tok/s) and still scores well for agentic workflows (88.4/100).
- How fast is gemma-4-26b-a4b for local inference?
- Across 2 community benchmark runs, gemma-4-26b-a4b reaches up to 117.4 tok/s and averages 79.6 tok/s, with the fastest results on NVIDIA GeForce RTX 5080.
- How much memory does gemma-4-26b-a4b need?
- The leanest observed configuration used about 16.7 GB of memory.
- Which tools have been used to run gemma-4-26b-a4b?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.