Model benchmarks
PentaCoder-9B-Q8_0 local LLM performance
As of October 2026, PentaCoder-9B-Q8_0 runs at up to 74.4 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
74.4 tok/s
Average speed
70.3 tok/s
Avg PP
595.7 tok/s
Min memory
18.0 GB
Max context
65,536 tokens
Avg output / run
18,886 tokens
Avg runtime / run
5m 14s
Avg quality
74.9
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 | 74.6 | 74.9 | 75.2 |
| Agent Workflow | 71.4 | 76.8 | 82.2 |
| Code Generation | 69.6 | 71.1 | 72.6 |
| Role Play & Narrative | 72.4 | 76.5 | 80.6 |
| Research & Analysis | 73.8 | 75.2 | 76.7 |
Performance by hardware and tool
Every hardware/tool/quantization combination PentaCoder-9B-Q8_0 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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5070 Ti | llama.cpp | Q8_0 | 74.4 tok/s | 70.3 tok/s | 18.0 GB | 65,536 tokens | 74.9 | 2 |
Benchmark runs
All 2 PentaCoder-9B-Q8_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is PentaCoder-9B-Q8_0 good for coding?
- In our benchmarks, PentaCoder-9B-Q8_0 scores 71.1/100 for coding. It runs at about 70.3 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 PentaCoder-9B-Q8_0 good for agentic (tool-using) tasks?
- In our benchmarks, PentaCoder-9B-Q8_0 scores 76.8/100 for agentic workflows. It runs at about 70.3 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 PentaCoder-9B-Q8_0 for local inference?
- Across 2 community benchmark runs, PentaCoder-9B-Q8_0 reaches up to 74.4 tok/s and averages 70.3 tok/s, with the fastest results on NVIDIA GeForce RTX 5070 Ti.
- How much memory does PentaCoder-9B-Q8_0 need?
- The leanest observed configuration used about 18.0 GB of memory (quantizations tested: Q8_0).
- Which tools have been used to run PentaCoder-9B-Q8_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.