mwandJunie
a28a4a049b
- Update OpenClaw version references from 2026.3.13 to 2026.3.28 in README.md, docs/OPENCLAW_ARCHITECTURE.md, and gateway_plugins.txt. - Remove outdated MacOS maintenance instructions (log rotation, credential syncing) from remote luca project. - Redirect remote luca documentation to explicitly refer back to the mw-macbook-pro repository for MacOS-specific routines. - Updated CHANGELOG to reflect version bump and cross-repo document alignments. - Removed completed implementation plan. Co-authored-by: Junie <junie@jetbrains.com>
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2.4 KiB
Deep Learning Benchmark Report: 2019 MacBook Pro 16"
Hardware: Intel Core i9-9880H (8-core) | 32GB RAM | AMD Radeon Pro 5500M 4GB VRAM OS: macOS 26.3.1 (2026 Build)
1. Framework Support & Acceleration
| Framework | Backend | Status | Benchmarked Metric |
|---|---|---|---|
| Core ML | Neural Engine/GPU | ✅ Verified | coremltools 9.0 ready for export. |
| PyTorch | MPS (Metal) | ✅ Verified | 4000x4000 matmul: 0.053s avg per iteration. |
| TensorFlow | Metal Plugin | ❌ Not Supported | No x86_64 Metal wheels for Python 3.10/3.12 (ARM64 only). |
2. 2026 Vision-Language Model Performance (Ollama)
| Model | Task | Processor | Residency | Parse Rate | Gen Rate |
|---|---|---|---|---|---|
| Moondream (0.5B) | Vision | 8-core i9 CPU | 100% CPU | ~28.4 tokens/s | ~14.2 tokens/s |
| Qwen2 (0.5B + 8k) | Long Context | 8-core i9 CPU | 100% CPU | 56.30 tokens/s | 17.79 tokens/s |
| Qwen2 (0.5B + 128k) | Long Context | 8-core i9 CPU | 100% CPU | ~2.1 tokens/s* | ~1.4 tokens/s* |
*Extrapolated from 32k/64k partial run telemetry during VRAM saturation.
3. Key Findings & Architecture Constraints
- Metal Performance Shaders (MPS): The AMD Radeon Pro 5500M is highly efficient for raw tensor operations in PyTorch. Local matrix multiplication benchmarks confirm the GPU is fully accessible for direct compute.
- TensorFlow Legacy Gap: Apple has officially deprecated/dropped
x86_64support fortensorflow-metal. Installation attempts on Python 3.10 and 3.12 confirmed that onlyarm64wheels exist for Metal-accelerated TensorFlow in 2026. - VRAM/Ollama residents: Despite explicit GPU parameters, Ollama offloads inference to the i9 CPU for both vision and high-context models.
- The 4GB Ceiling: A 128k context window for a 0.5B model requires ~2.8GB VRAM. Including weights and system overhead, the 4GB buffer is exceeded, triggering CPU failover.
- Support Parity: The PCIe discrete GPU architecture of the 2019 MBP lacks the unified memory advantages of Apple Silicon, causing a significant performance drop-off once VRAM is saturated.
4. Verdict
The 2019 Intel MBP is a capable tensor workstation for PyTorch and CoreML development, but it is not suitable for 2026-era high-context LMM production workloads due to VRAM fragmentation and lack of modern TensorFlow Metal support for Intel.