Model benchmarks
hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL local LLM performance
As of September 2026, hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL runs at up to 79.9 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
7.46B
Peak speed
79.9 tok/s
Average speed
79.7 tok/s
Avg PP
3111.7 tok/s
Min memory
2.8 GB
Max context
131,072 tokens
Avg output / run
9,239 tokens
Avg runtime / run
2m 13s
Avg quality
64.8
Benchmark runs
4
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 4 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 | 60.6 | 64.8 | 69.8 |
| Agent Workflow | 63.6 | 74.1 | 80.8 |
| Code Generation | 43.2 | 52.9 | 60.0 |
| Role Play & Narrative | 59.3 | 71.5 | 81.7 |
| Research & Analysis | 55.6 | 60.8 | 64.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination hf.co/unsloth/gemma-4-E4B-it-qat-GGUF: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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 2070 SUPER | Ollama | Q4_0 | 79.9 tok/s | 79.7 tok/s | 2.8 GB | 131,072 tokens | 64.8 | 4 |
Benchmark runs
All 4 hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL good for coding?
- In our benchmarks, hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL scores 52.9/100 for coding. It runs at about 79.7 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 hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL scores 74.1/100 for agentic workflows. It runs at about 79.7 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS is faster (~101.8 tok/s) and still scores well for agentic workflows (88.4/100).
- How fast is hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL for local inference?
- Across 4 community benchmark runs, hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL reaches up to 79.9 tok/s and averages 79.7 tok/s, with the fastest results on NVIDIA GeForce RTX 2070 SUPER.
- How much memory does hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL need?
- The leanest observed configuration used about 2.8 GB of memory (quantizations tested: Q4_0).
- Which tools have been used to run hf.co/unsloth/gemma-4-E4B-it-qat-GGUF:UD-Q4_K_XL?
- Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.