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

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.

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.