Model benchmarks
google/gemma-4-26b-a4b-qat local LLM performance
As of August 2026, google/gemma-4-26b-a4b-qat runs at up to 51.4 tok/s for local inference (best of 3 community benchmark runs across 2 GPUs).
Model size
26B
Peak speed
51.4 tok/s
Average speed
33.6 tok/s
Min memory
12.7 GB
Max context
32.768 tokens
Avg output / run
17.988 tokens
Avg runtime / run
9m 2s
Avg quality
67.2
Benchmark runs
3
GPUs tested
2
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 3 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 | 61.7 | 67.2 | 71.5 |
| Agent Workflow | 27.7 | 55.7 | 76.5 |
| Code Generation | 62.0 | 63.9 | 65.2 |
| Role Play & Narrative | 80.8 | 83.1 | 84.5 |
| Research & Analysis | 60.3 | 66.0 | 73.8 |
Performance by hardware and tool
Every hardware/tool/quantization combination google/gemma-4-26b-a4b-qat has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 4070 Ti SUPER | LM Studio | — | 51.4 tok/s | 51.4 tok/s | 14.6 GB | 16.384 tokens | 68.8 | 1 |
| Apple M5 | LM Studio | — | 33.8 tok/s | 33.8 tok/s | 12.7 GB | 8.192 tokens | 60.9 | 1 |
| CPU only | LM Studio | — | 15.7 tok/s | 15.7 tok/s | 12.7 GB | 32.768 tokens | 71.8 | 1 |
Benchmark runs
All 3 google/gemma-4-26b-a4b-qat runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is google/gemma-4-26b-a4b-qat good for coding?
- In our benchmarks, google/gemma-4-26b-a4b-qat scores 63.9/100 for coding. It runs at about 33.6 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
- Is google/gemma-4-26b-a4b-qat good for agentic (tool-using) tasks?
- In our benchmarks, google/gemma-4-26b-a4b-qat scores 55.7/100 for agentic workflows. It runs at about 33.6 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is google/gemma-4-26b-a4b-qat for local inference?
- Across 3 community benchmark runs, google/gemma-4-26b-a4b-qat reaches up to 51.4 tok/s and averages 33.6 tok/s, with the fastest results on NVIDIA GeForce RTX 4070 Ti SUPER.
- How much memory does google/gemma-4-26b-a4b-qat need?
- The leanest observed configuration used about 12.7 GB of memory.
- Which tools have been used to run google/gemma-4-26b-a4b-qat?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.