Model benchmarks

qwen3:14b local LLM performance

As of August 2026, qwen3:14b runs at up to 33.4 tok/s for local inference (best of 4 community benchmark runs across 2 GPUs).

OllamaQ4_K_M
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Model size

14.8B

Peak speed

33.4 tok/s

Average speed

18.7 tok/s

Min memory

10.0 GB

Max context

196,608 tokens

Avg runtime / run

13m 8s

Avg quality

59.4

Benchmark runs

4

GPUs tested

2

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.

TaskP5 (low)AvgP95 (high)
Overall51.759.463.8
Agent Workflow56.371.480.4
Code Generation4.030.654.4
Role Play & Narrative71.676.279.5
Research & Analysis50.059.272.7

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3:14b has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
AMD Radeon RX 7700 XT / 7800 XTOllamaQ4_K_M33.4 tok/s33.4 tok/s15.2 GB196,608 tokens64.21
AMD Radeon RX 7900 XTXOllamaQ4_K_M16.5 tok/s13.7 tok/s10.0 GB65,536 tokens57.73

Frequently asked questions

Is qwen3:14b good for coding?
In our benchmarks, qwen3:14b scores 30.6/100 for coding. It runs at about 18.7 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for coding (66.9/100).
Is qwen3:14b good for agentic (tool-using) tasks?
In our benchmarks, qwen3:14b scores 71.5/100 for agentic workflows. It runs at about 18.7 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for agentic workflows (71.7/100).
How fast is qwen3:14b for local inference?
Across 4 community benchmark runs, qwen3:14b reaches up to 33.4 tok/s and averages 18.7 tok/s, with the fastest results on AMD Radeon RX 7700 XT / 7800 XT.
How much memory does qwen3:14b need?
The leanest observed configuration used about 10.0 GB of memory (quantizations tested: Q4_K_M).
Which tools have been used to run qwen3:14b?
Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.