Search for a command to run...
Crafting seamless user experiences with a passion for headless CMS, Vercel deployments, and Cloudflare optimization. I'm a Full Stack Developer with expertise in building modern web applications that are blazing fast, secure, and scalable. Let's connect and discuss how I can help you elevate your next project!
No comments yet. Be the first to comment.
A Codex long-running agent workflow succeeds when responsibility, state, wake conditions, acceptance criteria, and stopping boundaries remain visible and reviewable. OpenAI developer experience lead J
The Codex–ChatGPT Pro dual-agent workflow separates implementation from acceptance; it does not prove that one AI system is the strongest coding agent. Codex can hold the repository, requirements, per
Japan's physical AI strategy entered an execution phase in 2026, with a national model program running through 2030. The government is funding factory-data preparation and robotics foundation models.
The AI infrastructure bubble may stay contained while chips, power, and construction capacity remain scarce. In July 2026, NVIDIA CEO Jensen Huang told Axios that a bubble was highly unlikely within f
Firecrawl gives AI agents a programmable web-context layer for search, extraction, crawling, document parsing, and browser interaction. It can remove a large amount of scraping glue code, but it does
The M4 shows significant improvements over the M1 series, particularly in:
Single-Core Performance
64% faster in single-core Geekbench v6 tests (3810 vs 2328 points)
Notably higher base frequency at 4.41 GHz compared to M1's 3.2 GHz
Multi-Core Tasks
Superior performance in various benchmarks:
21% faster in file compression (1490 MB/sec vs 1230 MB/sec)
38% faster in HTML 5 browser tasks (340.7 vs 249.1 pages/sec)
44% improvement in photo processing (147.6 vs 102.5 images/sec)
Architecture and Manufacturing
M4 uses a more advanced 3nm manufacturing process versus M1's 5nm
M4 features the newer ARMv9 instruction set compared to M1's ARMv8
Core Configuration
M4: 4 P-cores (4.41 GHz) + 6 E-cores (2.89 GHz)
Total of 10 cores and 10 threads
Memory and Bandwidth
M4 supports LPDDR5X-7500 memory up to 24GB
M1 Pro offers higher memory bandwidth at 204.8 GB/s compared to M4's 120 GB/s
Integrated GPU Comparison
M4 GPU: 1280 shading units, 1600 MHz boost clock
Base TGP of 15W for graphics processing
M1 Pro GPU offers higher graphics performance at 5.3 TFLOPS compared to M4's 4.1 TFLOPS
Thermal Design
M4 has a lower TDP of 10W (base) and 20W (boost)
More efficient thermal performance with 3nm process
Maximum operating temperature of 100°C
Overall, the M4 represents a significant generational improvement over the M1, offering up to 1.8x faster performance for tasks like editing gigapixel photos.
external display capabilities of the MacBook Pro with M4 Max:
The MacBook Pro with M4 Max can support:
Up to four external displays in total
Configuration options include:
Up to three external displays with 6K resolution at 60Hz over Thunderbolt, plus one additional external display with up to 4K resolution at 144Hz over HDMI
Alternatively, up to two 6K displays at 60Hz over Thunderbolt, plus one display with up to 8K resolution at 60Hz or one 4K display at 240Hz over HDMI
Thunderbolt Connectivity
Features Thunderbolt 5 ports for enhanced display support
Native DisplayPort output over USB-C
Support for billions of colors
HDMI Output Options
HDMI digital video output supporting:
8K resolution at 60Hz
4K resolution at up to 240Hz
This represents a significant improvement in external display support compared to previous generations, offering users more flexibility in their multi-display setups with higher resolutions and refresh rates.
| Feature | M4 Max | M1 Max |
| CPU Cores | 14-16 cores (10P + 4E) | 10 cores (8P + 2E) |
| Base Clock | 4.41 GHz (P-cores) | 3.2 GHz (P-cores) |
| GPU Cores | Up to 40 cores | 32 cores |
| Neural Engine | 16-core | 16-core |
| Memory Bandwidth | 400 GB/s | 409.6 GB/s |
| Manufacturing Process | 3nm | 5nm |
| External Display Support | Up to 3x 6K + 1x 4K | Up to 4x displays |
| Media Engine | 2 video encode engines, 2 ProRes engines, AV1 decode | 2 video encode engines, 2 ProRes engines |
| Ray Tracing | Yes, hardware-accelerated | No |
| Memory Support | Up to 128GB unified memory | Up to 64GB unified memory |
MacBook Pro M4 Max with 128GB RAM for edge LLM development:
Processing Capabilities
Advanced 3nm manufacturing process for better efficiency
Higher single-core performance with 4.41 GHz base frequency
Improved multi-threading capabilities with 10 cores (4 P-cores + 6 E-cores)
Memory Benefits
Support for up to 128GB unified memory
LPDDR5X-7500 memory with quad-channel support
120 GB/s memory bandwidth
Model Optimization
Utilize approximately 75% of total RAM for GPU operations
Load smaller quantized models for edge deployment
Take advantage of the hardware-accelerated ray tracing capabilities
Performance Optimizations
Leverage improved file compression (1490 MB/sec)
Enhanced data encryption capabilities (15.4 GB/sec)
Utilize the integrated GPU with 1280 shading units for parallel processing
Key Development Features
Support for Metal API for GPU acceleration
Ability to run multiple smaller models simultaneously
Efficient quantization and model compression capabilities
Enhanced thermal management with 10W base TDP
The M4 Max's combination of high memory capacity, efficient processing cores, and advanced GPU capabilities makes it particularly well-suited for edge LLM development and deployment scenarios.
how to effectively use MacBook Pro with M3 Max /M4 Max for running Llama 3 large models:
70B Model Limitations
The 70B model requires approximately 140GB RAM for unquantized operation
Cannot run full 70B model unquantized on 128GB MacBook Pro
Needs a 192GB M2 Ultra Mac Pro or Studio for unquantized operation
Quantized Performance
4-bit OmniQuant version (gs=128) achieves ~8.42 tokens/sec
Q6_K quantized version runs at 4.5-5.5 tokens per second
Q8_0 quantization maintains similar performance at 4.7 tokens per second
Optimization Tips
Use quantized versions for better memory efficiency
Take advantage of Metal Performance Shaders (MPS) for M-series chips
Consider using the 8B model for better performance if full 70B model isn't necessary
Available Versions
Llama 3 8B: More efficient for local deployment
Llama 3 70B: Requires quantization for 128GB systems
New tokenizer with 128K vocabulary for improved efficiency
For optimal performance on MacBook Pro with 128GB RAM, it's recommended to use quantized versions of the larger models or the smaller 8B model for development and testing purposes.
Analysis of how the M4 Max's GPU handles Llama 3's computational demands:
Memory Specifications
Expected memory bandwidth of 480-550 GB/s
Significantly higher than M3 Max's 400 GB/s
Still lower than NVIDIA RTX 3090's 936 GB/s
Comparative Performance
For 8B quantized models (Q4):
RTX 3090 achieves approximately 111.74 tokens/second
M3 Max achieves approximately 50.74 tokens/second
M4 Max's improved memory bandwidth suggests better performance than M3 Max, but still likely lower than high-end NVIDIA GPUs
AI Acceleration
Features Apple's fastest Neural Engine capable of 38 trillion operations per second
Hardware-accelerated ray tracing and mesh shading
Next-generation machine learning accelerators in CPU
Enhanced memory bandwidth for AI workloads
For Optimal Performance
Use quantized versions of larger models
Take advantage of Metal Performance Shaders (MPS)
Consider context length limitations based on available memory
Leverage the Neural Engine for AI acceleration
While the M4 Max shows significant improvements over previous generations, it may not match dedicated GPU solutions like the RTX 3090 for large language model inference, but offers excellent power efficiency and integrated AI acceleration capabilities.
