Model benchmarks
inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M local LLM performance
As of September 2026, inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M runs at up to 55.7 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
Unknown
Peak speed
55.7 tok/s
Average speed
44.3 tok/s
Avg PP
356.3 tok/s
Min memory
n/a
Max context
65.536 tokens
Avg output / run
39.463 tokens
Avg runtime / run
14m 33s
Avg quality
54.5
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 | 51.3 | 54.5 | 57.7 |
| Agent Workflow | 63.7 | 69.4 | 75.0 |
| Code Generation | 1.9 | 18.8 | 35.6 |
| Role Play & Narrative | 60.3 | 68.6 | 76.8 |
| Research & Analysis | 51.5 | 61.4 | 71.3 |
Performance by hardware and tool
Every hardware/tool/quantization combination inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M 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 2060 | llama.cpp | Q4_K_M | 55.7 tok/s | 55.7 tok/s | 3.4 GB | 65.536 tokens | 58.1 | 1 |
| NVIDIA GeForce RTX 2060 | OpenAI-compatible | Q4_K_M | 33.0 tok/s | 33.0 tok/s | n/a | 8.192 tokens | 50.9 | 1 |
Benchmark runs
All 2 inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M good for coding?
- In our benchmarks, inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M scores 18.8/100 for coding. It runs at about 44.3 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 inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M good for agentic (tool-using) tasks?
- In our benchmarks, inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M scores 69.4/100 for agentic workflows. It runs at about 44.3 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M for local inference?
- Across 2 community benchmark runs, inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M reaches up to 55.7 tok/s and averages 44.3 tok/s, with the fastest results on NVIDIA GeForce RTX 2060.
- How much memory does inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M need?
- The leanest observed configuration used about n/a of memory (quantizations tested: Q4_K_M).
- Which tools have been used to run inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M?
- Benchmarks were submitted using OpenAI-compatible, llama.cpp. Results are community-contributed and updated as new runs arrive.