for Local LLM Inference
TL;DR
For local LLM inference, the M4 Max delivers competitive performance with massive unified memory (128GB+), but the RTX 4090 dominates raw throughput and cost-effectiveness for up to 24GB models. The 4090 wins for speed and affordability, while the M4 Max is the only choice for huge models that don't fit in 24GB VRAM.
It depends on your workload. For local LLM inference, the M4 Max delivers competitive performance with massive unified memory (128GB+), but the RTX 4090 dominates raw throughput and cost-effectiveness for up to 24GB models. The 4090 wins for speed and affordability, while the M4 Max is the only choice for huge models that don't fit in 24GB VRAM.
Winner: VRAM / Unified Memory
Apple M4 Max
Winner: Memory Bandwidth
NVIDIA RTX 4090
Winner: Peak FP16 TFLOPS
NVIDIA RTX 4090
Overall Pick
It Depends
| Specification | Apple M4 Max | NVIDIA RTX 4090 |
|---|---|---|
| VRAM / Unified Memory | Up to 128GB unified | 24GB GDDR6X |
| Memory Bandwidth | ~800 GB/s (M4 Max 128GB) | 1008 GB/s |
| Peak FP16 TFLOPS | ~27 TFLOPS (estimated) | 82.6 TFLOPS (tensor cores) |
| INT8 TOPS | ~54 TOPS | 660 TOPS (sparse) |
| Model Size Limit (4-bit) | ~90-100B params (128GB) | ~13B params (24GB) |
| Inference Speed (7B Q4, 4096 ctx) | ~50-70 t/s | ~120-150 t/s |
| Inference Speed (70B Q4, 4096 ctx) | ~8-12 t/s (128GB) | Cannot run |
| Prompt Processing (7B, 2048 tokens) | ~800-1200 t/s | ~2500-3500 t/s |
| Context Window (unquantized) | Unlimited (within memory) | Limited (VRAM) |
| Power Consumption (peak) | ~40-50W (whole system) | 450W (GPU only) |
| System Price (typical) | $4,000-$5,500 (Mac Studio) | $1,600-$2,000 (GPU + PC) |
| Multi-GPU Support | No | Yes (NVLink, limited) |
| Software Ecosystem | MLX, llama.cpp, growing | CUDA, TensorRT, llama.cpp |
Cross-referenced from our benchmark database , higher is better. Numbers are tokens/sec for LLM workloads, images/min for image workloads.
If you mostly
run Mixtral 8x22B (141B) at Q4_K_M daily
Recommend
Apple M4 Max
The M4 Max 128GB fits this model with ease, whereas the RTX 4090's 24GB VRAM cannot load it at all. You will get ~5-8 t/s on Apple Silicon, which is usable for local inference.
If you mostly
need maximum speed for Llama 3 8B or CodeLlama 13B with batch requests
Recommend
NVIDIA RTX 4090
The RTX 4090 delivers 2-3x faster tokens per second and massively faster prompt processing, making it better for real-time chatbots and coding assistants. The M4 Max is slower and more expensive for these smaller models.
If you mostly
want a silent, low-power system for 70B model experimentation but don't need real-time speed
Recommend
Apple M4 Max
The M4 Max Mac Studio can run 70B models (Q4) at ~10 t/s while consuming under 50W and staying silent. The RTX 4090 would require a loud desktop build and cannot run 70B at all without quantization to 2-bit (poor quality).
The RTX 4090 offers vastly better perf/dollar for models up to 13B parameters, costing ~$1,600 for a GPU that crushes inference speeds, while the cheapest M4 Max with 128GB starts above $4,000. However, the M4 Max achieves superior perf/watt (under 50W for 10 t/s on 70B) and has lower total cost of ownership if your workload demands models beyond 24GB, you'd need multiple 4090s or a $10k+ A6000 to match the Mac's memory pool. For most local LLM builders focused on sub-20B models, the 4090 wins on value; for those who must run 70B+ models locally, the M4 Max is the most cost-effective single-system option.
Apple M4 Max
NVIDIA RTX 4090
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Apple M4 Max
Not currently available on Amazon, check manufacturer or B2B reseller.
The M4 Max vs RTX 4090 decision for local LLMs isn't about which is 'better', it's about which problem you're solving. If your entire workflow fits within 24GB VRAM, the RTX 4090 is absurdly more powerful, cheaper, and better supported by the CUDA ecosystem. You get faster token generation, quicker prompt processing, and the ability to scale via multi-GPU setups if needed. The power draw and heat are real, but for performance per dollar, the 4090 demolishes the M4 Max in its lane.
The M4 Max's killer argument is the unified memory capacity. No consumer GPU can match 128GB of fast memory shared between CPU and GPU, and that enables running large quantized models (70B, 120B, 140B) that are impossible on a 4090. For researchers, tinkerers, or anyone who needs local access to big models without cloud costs, the Mac Studio is the only game in town below $10k. Just know you sacrifice raw throughput, software maturity, and upgradeability. Choose based on your model size ceiling, not a spec sheet race.
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