u/PiqueForPresident

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.