Model benchmarks
gemma-4-26b-a4b-q4kxl local LLM performance
As of September 2026, gemma-4-26b-a4b-q4kxl runs at up to 171.1 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
26B
Peak speed
171.1 tok/s
Average speed
169.9 tok/s
Min memory
12.7 GB
Max context
65,536 tokens
Avg output / run
15,944 tokens
Avg runtime / run
1m 34s
Avg quality
69.6
Benchmark runs
3
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 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 | 65.4 | 69.6 | 73.7 |
| Agent Workflow | 73.1 | 75.2 | 78.1 |
| Code Generation | 64.1 | 67.9 | 73.3 |
| Role Play & Narrative | 69.1 | 79.9 | 87.1 |
| Research & Analysis | 46.2 | 55.3 | 65.3 |
Performance by hardware and tool
Every hardware/tool/quantization combination gemma-4-26b-a4b-q4kxl 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 |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M | llama.cpp | — | 171.1 tok/s | 169.9 tok/s | 12.7 GB | 65,536 tokens | 69.6 | 3 |
Frequently asked questions
- Is gemma-4-26b-a4b-q4kxl good for coding?
- In our benchmarks, gemma-4-26b-a4b-q4kxl scores 67.9/100 for coding. It runs at about 169.9 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for coding (66.9/100).
- Is gemma-4-26b-a4b-q4kxl good for agentic (tool-using) tasks?
- In our benchmarks, gemma-4-26b-a4b-q4kxl scores 75.2/100 for agentic workflows. It runs at about 169.9 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for agentic workflows (71.7/100).
- How fast is gemma-4-26b-a4b-q4kxl for local inference?
- Across 3 community benchmark runs, gemma-4-26b-a4b-q4kxl reaches up to 171.1 tok/s and averages 169.9 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M.
- How much memory does gemma-4-26b-a4b-q4kxl need?
- The leanest observed configuration used about 12.7 GB of memory.
- Which tools have been used to run gemma-4-26b-a4b-q4kxl?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.