Model benchmarks

Tiel-Coder-35B-A3B-Q4_K_S local LLM performance

As of September 2026, Tiel-Coder-35B-A3B-Q4_K_S runs at up to 166.6 tok/s for local inference (best of 5 community benchmark runs across 1 GPU).

llama.cppQ4_K_S
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Model size

35B

Peak speed

166.6 tok/s

Average speed

162.4 tok/s

Min memory

n/a

Max context

65,536 tokens

Avg output / run

27,626 tokens

Avg runtime / run

2m 47s

Avg quality

85.1

Benchmark runs

5

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 5 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

TaskP5 (low)AvgP95 (high)
Overall83.985.187.0
Agent Workflow82.187.892.0
Code Generation75.379.282.7
Role Play & Narrative83.488.993.1
Research & Analysis83.084.686.9

Performance by hardware and tool

Every hardware/tool/quantization combination Tiel-Coder-35B-A3B-Q4_K_S has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
AMD Radeon RX 7900 XTXllama.cppQ4_K_S166.6 tok/s162.4 tok/sn/a65,536 tokens85.15

Benchmark runs

All 5 Tiel-Coder-35B-A3B-Q4_K_S runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

Is Tiel-Coder-35B-A3B-Q4_K_S good for agentic (tool-using) tasks?
In our benchmarks, Tiel-Coder-35B-A3B-Q4_K_S scores 87.8/100 for agentic workflows. It runs at about 162.4 tok/s, so if you want more speed, mudler/Ornith-1.5-35B-A3B-APEX-MTP-GGUF is faster (~190.9 tok/s) and still scores well for agentic workflows (85.7/100).
How fast is Tiel-Coder-35B-A3B-Q4_K_S for local inference?
Across 5 community benchmark runs, Tiel-Coder-35B-A3B-Q4_K_S reaches up to 166.6 tok/s and averages 162.4 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
Which tools have been used to run Tiel-Coder-35B-A3B-Q4_K_S?
Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.