Model benchmarks

Qwen3.6-35B-A3B-Q4_K_XL local LLM performance

As of August 2026, Qwen3.6-35B-A3B-Q4_K_XL runs at up to 159.0 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).

llama.cppQ4_K
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Model size

35B

Peak speed

159.0 tok/s

Average speed

156.9 tok/s

Min memory

17.1 GB

Max context

65,536 tokens

Avg output / run

17,702 tokens

Avg runtime / run

1m 55s

Avg quality

84.0

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.

TaskP5 (low)AvgP95 (high)
Overall83.984.084.1
Agent Workflow76.984.491.9
Code Generation80.282.284.2
Role Play & Narrative84.886.688.4
Research & Analysis79.682.886.0

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.6-35B-A3B-Q4_K_XL has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900Mllama.cppQ4_K159.0 tok/s156.9 tok/s17.1 GB65,536 tokens84.02

Frequently asked questions

How fast is Qwen3.6-35B-A3B-Q4_K_XL for local inference?
Across 2 community benchmark runs, Qwen3.6-35B-A3B-Q4_K_XL reaches up to 159.0 tok/s and averages 156.9 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M.
How much memory does Qwen3.6-35B-A3B-Q4_K_XL need?
The leanest observed configuration used about 17.1 GB of memory (quantizations tested: Q4_K).
Which tools have been used to run Qwen3.6-35B-A3B-Q4_K_XL?
Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.