Can I use this comfortably?
Comfortable for agentic coding
Yes — this should be comfortable for agentic coding.
Experience breakdown
≈16 GB headroom
Importance for this workload: critical. Estimated peak ≈32 GB of 48 GB; ≈16 GB remains for the OS, IDE, browser, terminal and your coding agent to grow into.
30–45 tok/s
Importance for this workload: critical. Expected 30–45 tok/s at ~21K tokens of context. Agentic coding is comfortable from about 35 tok/s.
≈30 s first prompt
Importance for this workload: critical. Ingesting the first prompt (18K tokens) takes ≈30 s; each of the ~25 follow-up agent steps takes ≈15 s (≈7 min per task).
32K context
Importance for this workload: very high. 32K window; generation drops to 25–35 tok/s when it is full.
Official
Importance for this workload: critical. OpenCode → OpenAI-compatible API → LM Studio. Configure a provider with @ai-sdk/openai-compatible and the local base URL.
Partly suited
Importance for this workload: critical. Coding capability is below what agentic coding ideally needs — expect more mistakes or retries. Tool calling is reliable.
Runtime, concurrency and sustained-use factors
LM Studio · Metal (Apple GPU)
Importance for this workload: high. LM Studio uses Metal (Apple GPU) on macOS (mature support).
Single stream
Importance for this workload: medium. One request at a time.
Stable
Importance for this workload: high. No sustained-use concerns.
Why “Comfortable”?
Why?
- Model fits with approximately 16 GB of memory headroom.
- LM Studio supports hardware acceleration on this machine (Metal (Apple GPU)).
- OpenCode can connect to LM Studio (officially supported).
- Generation (30–45 tok/s) should be appropriate for agentic coding.
- Ingesting a prompt of 18K tokens takes ≈30 s; prompt processing keeps up with agentic coding.
- 32K working context is reasonable.
- Coding capability is below what agentic coding ideally needs — expect more mistakes or retries. Tool calling is reliable.
Why “Comfortable” rather than “Excellent”?
- Workload suitability: Coding capability is below what agentic coding ideally needs — expect more mistakes or retries. Tool calling is reliable.
- Prompt processing: Ingesting the first prompt (18K tokens) takes ≈30 s; each of the ~25 follow-up agent steps takes ≈15 s (≈7 min per task).
- Context: 32K window; generation drops to 25–35 tok/s when it is full.
- Generation: Expected 30–45 tok/s at ~21K tokens of context. Agentic coding is comfortable from about 35 tok/s.
Better for
- • Interactive chat
- • Interactive coding
Less suitable for
- • Multiple simultaneous agents
How sure are we?
Medium confidenceEstimated from bandwidth and compute, calibrated against 2 verified benchmarks of other models on this same chip (measured with a different engine, so calibration is partial).
- Calibrated with verified benchmarks of other models on this chip.
- MoE performance varies more between runtimes.
Memory
Not all installed memory is available: the OS, your other apps and the tool come first. Unified memory is one pool shared by CPU and GPU.
GPU may use up to ≈36 GB of unified memory at the current setting.
- Installed memory
- 48 GB
- Reserved for OS
- 4.8 GB
- Other apps / dev environment
- 6.0 GB
- AI tool process
- 0.6 GB
- Runtime overhead & buffers
- 1.0 GB
- Model weights
- 16 GB
- KV cache
- 3.0 GB
- Estimated peak (inference)
- 20 GB
- Remaining headroom
- 16 GB
Performance estimateCalibrated estimate
Estimated from bandwidth and compute, calibrated against 2 verified benchmarks of other models on this same chip (measured with a different engine, so calibration is partial). Ranges reflect estimation uncertainty.
- Generation, short context
- 65–90 tok/s
- Generation, typical context
- 30–45 tok/s
- Generation, full context
- 25–35 tok/s
- Prompt processing (prefill)
- 740–1000 tok/s
- First (cold) prompt
- ≈30 s
- Time to first token (follow-up)
- ≈7 s
- Per turn / agent step
- ≈15 s
- Typical task
- ≈7 min
- Model load time
- ≈6 s
- Backend
- METAL
Tokens/sec alone doesn’t describe the experience: an agent repeatedly ingests large prompts, so prompt processing and per-step latency matter as much as generation speed.
Community measurements
Nobody has reported this model on MacBook Pro M4 Pro 48 GB yet. Measured it yourself? Your numbers improve the estimates for everyone with this chip.
What happens at different context sizes?
Same hardware, model and workload — only the context window changes. Larger contexts grow the KV cache and slow generation.
| Context | Rating for this workload | Generation when full | First prompt | Headroom |
|---|---|---|---|---|
| 4K | Acceptable | ≈67 tok/s | ≈5 s | 19 GB |
| 8K | Acceptable | ≈57 tok/s | ≈10 s | 19 GB |
| 16K | Acceptable | ≈44 tok/s | ≈25 s | 18 GB |
| 32K (selected) | Comfortable | ≈30 tok/s | ≈30 s | 16 GB |
| 64K | Acceptable | ≈19 tok/s | ≈30 s | 13 GB |
| 128K | Borderline | ≈11 tok/s | ≈30 s | 7.0 GB |
Same setup, other workloads
The same model on the same machine earns different ratings depending on what you do with it.
- Casual chatExcellent
- Coding questionsExcellent
- Interactive coding on a normal repositoryComfortable
- Agentic coding (medium repository)Comfortable
- Agentic coding on very large repositoriesAcceptable
- 64K+ long-document Q&AComfortable
- ReasoningAcceptable
- Multiple simultaneous agentsAcceptable
- Serving several concurrent usersAcceptable
Your local AI stack
The tool never runs the model itself — it talks to a runtime through an API.
AI tool
OpenCode
Configure a provider with @ai-sdk/openai-compatible and the local base URL.
Local API
OpenAI-compatible endpoint
http://localhost:1234
Model
Qwen3-Coder 30B-A3B Instruct — 4-bit
16 GB weights · 32K context
Runtime
LM Studio
METAL backend · MLX weights
Hardware
MacBook Pro M4 Pro 48 GB
48 GB unified memory
Connection: OpenCode → OpenAI-compatible API → LM Studio — Official
Estimates use memory 48 GB unified · data last verified 2026-09-30. See methodology.