If you use Open Code or other agenting programs you are leaving a lot of t/s if you don't actually use agents in parallel. Benchmark : RTX5090, Qwen3.6 35B loaded via LM studio with parallel tasks set to 8

As many of you know t/s is super important. It's how fast your stuff gets done. I create via open code benchtest and run it. Thanks to it i know that if i don't run at least 4 agents i basically leave HALF of performance. So whatever you do single project in open code that uses one agent or 4 project at once it is much better to run it this way rather than single instance or single agent. I asked AI to do summary of my test and checked them: LM Studio Multi-Agent Throughput Benchmark Hardware: RTX 5090 Model: Qwen 3.6 35B (via LM Studio) Configured Parallel: 8 Test: 5 requests per agent, 1024 max tokens, temperature 0.3 Results Agents Avg t/s (each) Combined t/s Efficiency Wall Time 1 256.54 245.77 — 21.9s 2 176.15 346.22 70.4% 31.1s 3 134.73 398.64 54.1% 40.5s 4 109.77 434.34 44.2% 49.5s 5 95.04 470.34 38.3% 57.1s 6 84.73 504.14 34.2% 64.0s 7 74.49 517.68 30.1% 72.7s 8 67.22 533.90 27.2% 80.5s Key Findings Throughput Scaling Combined throughput grows sub-linearly: 8 agents yields only 2.2x combined throughput vs single (533 vs 245 t/s), not 8x. Individual agent speed drops sharply: From 256 t/s → 67 t/s as GPU compute is split across agents. Diminishing returns: 5→8 agents only adds ~63 t/s (12%). Most gains happen by agent 5. Efficiency Peak efficiency: 70.4% at 2 agents (closest to theoretical linear scaling). By agent 8: only 27.2% efficiency — two-thirds of potential throughput is lost to overhead. Sweet Spot: 4–5 Agents 4 agents: 434 t/s, 44% efficiency — good balance of speed and resource usage. 5 agents: 470 t/s, 38% efficiency — near peak combined throughput with acceptable overhead. Beyond 5: Marginal gains (517 t/s at 7, 533 at 8) for significant VRAM and overhead cost. How Concurrency Works with Context Each agent gets its own full context window — they do NOT split it. 8 agents × 8K context = 8x KV cache in GPU VRAM. The bottleneck is KV cache + compute splitting, not context size. Factor 1 Agent 8 Agents Model weights 1x 1x (shared) KV cache 1x 8x GPU compute 100% ~12.5% each VRAM pressure Low High Recommendation for OpenCode Set parallel agents to 4–5. This maximizes throughput gains without excessive VRAM overhead or diminishing returns. Beyond 5 agents, the combined throughput barely increases while resource consumption grows linearly.