Model benchmarks
unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL local LLM performance
As of October 2026, unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL runs at up to 64.7 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
26B
Peak speed
64.7 tok/s
Average speed
64.5 tok/s
Avg PP
685.5 tok/s
Min memory
n/a
Max context
65,536 tokens
Avg output / run
14,973 tokens
Avg runtime / run
4m 15s
Avg quality
65.5
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 | 63.8 | 65.5 | 67.3 |
| Agent Workflow | 69.5 | 74.5 | 79.5 |
| Code Generation | 63.3 | 65.4 | 67.4 |
| Role Play & Narrative | 67.9 | 72.4 | 76.8 |
| Research & Analysis | 40.2 | 49.8 | 59.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL 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 |
|---|---|---|---|---|---|---|---|---|
| Advanced Micro Devices, Inc. [AMD/ATI] Device 7551 | llama.cpp | Q5_K | 64.7 tok/s | 64.5 tok/s | n/a | 65,536 tokens | 65.5 | 2 |
Benchmark runs
All 2 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL good for coding?
- In our benchmarks, unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL scores 65.4/100 for coding. It runs at about 64.5 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~90.8 tok/s) and still scores well for coding (83.8/100).
- Is unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL scores 74.5/100 for agentic workflows. It runs at about 64.5 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
- How fast is unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL for local inference?
- Across 2 community benchmark runs, unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL reaches up to 64.7 tok/s and averages 64.5 tok/s, with the fastest results on Advanced Micro Devices, Inc. [AMD/ATI] Device 7551.
- Which tools have been used to run unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.