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QUEST-30B-RL

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QUEST-30B-RL

credit: Quest / qwen3moe
B70 input tok/s76.3
B70 decode tok/s335.5
B70 per-agent tok/s11.2
B70 live fleet32 · Intel Arc Pro B70

Q5_K_M qwen3moe · llama.cpp SYCL on Intel Arc Pro B70 · context-calibrated team of 6 · 15-min timed production loop.

model dossier

Model sheet

Playable artifacts and benchmark context for this model page.

Road Hopper
0 builds
Robot-Filled Maze Shooter
1 builds
Ribbit Rush
0 builds
Builds
1 recorded outputs
Method
team 6 / 15-min build
Runtime
llama.cpp SYCL on Intel Arc Pro B70
Quant
Q5_K_M qwen3moe
Bench
B70 live: 32 agents / 335.5 decode tok/sec
benchmark sheet

B70 live throughput profile

B70 input tok/s76.3
B70 decode tok/s335.5
B70 per-agent tok/s11.2
B70 live fleet32 · Intel Arc Pro B70
aggregate decode tok/secper-agent tok/sechighlight = selected live fleet
tool · 1 agents
66.5 dec84.6/agent · 154.2 in
tool · 8 agents
164.7 dec22.0/agent · 156.8 in
tool · 16 agents
223.0 dec14.8/agent · 121.0 in
tool · 32 agents
335.5 dec11.2/agent · 76.3 in
structured · 32 agents
211.2 dec7.5/agent · 101.0 in
novel · 32 agents
148.2 dec5.4/agent · 126.2 in

Highlighted row is the selected 32-agent tool-prompt live fleet on Intel Arc Pro B70. Structured and novel rows are LocalMaxxing prompt classes.

Raw B70 live table
classagentsprefill_tpsagg_decode_tpsper_agent_tpsbatch_wall_sstatus
tool1154.266.584.63.85OK
tool8156.8164.722.012.44OK
tool16121.0223.014.818.37OK
tool3276.3335.511.224.42OK
structured32101.0211.27.538.79OK
novel32126.2148.25.455.28OK

Generated game outputs

Versions are listed first for selection. Embedded outputs remain below for direct review.

Robot-Filled Maze Shooter 1 versions

maze_quest_b70_live · Robot-Filled Maze Shooter · team buildfullscreen ↗ · compare
Robot-Filled Maze Shooter team build Quest Turbo maze_quest_b70_livefullscreen ↗
UNDER RAPID CONSTRUCTION - WILL CHANGE
why

Local agents need public test loops

Working on local agents matters because these tests are the first step toward an R&D base-model trust score: a practical signal for choosing which architectures and models deserve deeper research and development.

Agentic Arcade is a playable hello world for that work. Anyone can open the games, review the artifacts, and compare model behavior. The mission is maximal distribution of intelligence to all people.