Model benchmarks
gemma-4-26B-A4B-it-qat-UD-Q4_K_XL local LLM performance
As of September 2026, gemma-4-26B-A4B-it-qat-UD-Q4_K_XL runs at up to 76.8 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
26B
Peak speed
76.8 tok/s
Average speed
70.8 tok/s
Avg PP
647.6 tok/s
Min memory
12.7 GB
Max context
65.536 tokens
Avg output / run
4.935 tokens
Avg runtime / run
1m 9s
Avg quality
67.0
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 | 64.9 | 67.0 | 69.1 |
| Agent Workflow | 57.4 | 66.4 | 75.5 |
| Code Generation | 58.6 | 61.2 | 63.8 |
| Role Play & Narrative | 79.0 | 79.8 | 80.6 |
| Research & Analysis | 57.8 | 60.6 | 63.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination gemma-4-26B-A4B-it-qat-UD-Q4_K_XL 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 |
|---|---|---|---|---|---|---|---|---|
| Radeon Instinct MI60 | llama.cpp | Q4_K | 76.8 tok/s | 70.8 tok/s | 12.7 GB | 65.536 tokens | 67.0 | 2 |
Benchmark runs
All 2 gemma-4-26B-A4B-it-qat-UD-Q4_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf good for coding?
- In our benchmarks, gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf scores 61.2/100 for coding. It runs at about 70.8 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 gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf good for agentic (tool-using) tasks?
- In our benchmarks, gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf scores 66.4/100 for agentic workflows. It runs at about 70.8 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS is faster (~102.1 tok/s) and still scores well for agentic workflows (88.7/100).
- How fast is gemma-4-26B-A4B-it-qat-UD-Q4_K_XL for local inference?
- Across 2 community benchmark runs, gemma-4-26B-A4B-it-qat-UD-Q4_K_XL reaches up to 76.8 tok/s and averages 70.8 tok/s, with the fastest results on Radeon Instinct MI60.
- How much memory does gemma-4-26B-A4B-it-qat-UD-Q4_K_XL need?
- The leanest observed configuration used about 12.7 GB of memory (quantizations tested: Q4_K).
- Which tools have been used to run gemma-4-26B-A4B-it-qat-UD-Q4_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.