Apple M4 Max 128GB vs RTX 5090 32GB for Local LLMs
The Apple M4 Max 128GB holds bigger models (96.0 GB vs 32.0 GB), and the RTX 5090 32GB generates tokens about 213% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| Apple M4 Max 128GB | RTX 5090 32GB | |
|---|---|---|
| Memory | 128 GB unified | 32 GB |
| GPU-usable memory | 96.0 GB | 32.0 GB |
| Bandwidth | 546 GB/s | 1792 GB/s |
| Architecture | Apple Silicon | Blackwell |
| Released | 2024-10 | 2025-01 |
| Llama 3.1 8B Q4_K_M | ~64 tok/s | ~202 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | Apple M4 Max 128GB | RTX 5090 32GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~64 tok/s | Fits ~202 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~36 tok/s | Fits ~115 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~107 tok/s | Fits ~221 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~86 tok/s | Fits ~146 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~17 tok/s | Fits ~54 tok/s |
| Llama 3.3 70B | 43.2 GB | Fits ~8.0 tok/s | 70% on GPU ~2.9 tok/s |
| gpt-oss-120b | 62.0 GB | Fits ~83 tok/s | 49% on GPU ~24 tok/s |
Verdict
Opposite strengths. The M4 Max holds about three times as much (around 96 GB GPU-usable), so it runs 70B models at Q8_0 and gpt-oss-120b, which the 5090 cannot. The 5090 has over three times the bandwidth and far more compute, so anything that fits in 32 GB runs about three times faster on it. Choose by the largest model you need.
Which should you buy?
Choose the Apple M4 Max 128GB if…
- You want 70B dense or 100B+ MoE models in one quiet box.
- You want a laptop or low-power desktop.
- Generation speed of 10–80 tokens/s is acceptable.
Choose the RTX 5090 32GB if…
- Your models fit in 32 GB and you want maximum speed.
- You generate images or video, or fine-tune models.
- You process long prompts and want fast time to first token.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | Apple M4 Max 128GB: best quant | RTX 5090 32GB: best quant | Apple M4 Max 128GB: max context | RTX 5090 32GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | FP16 / BF16 | Q8_0 | 256K | 219K |
| Qwen 3 32B | FP16 / BF16 | Q6_K | 40K | 40K |
| Llama 3.3 70B | Q8_0 | Q2_K | 128K | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | Apple M4 Max 128GB | RTX 5090 32GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP16 / BF16 | FP8 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Models only one card can run
Only Apple M4 Max 128GB
Only RTX 5090 32GB
None
Frequently asked questions
Is the Apple M4 Max 128GB or RTX 5090 32GB better for local LLMs?
Opposite strengths. The M4 Max holds about three times as much (around 96 GB GPU-usable), so it runs 70B models at Q8_0 and gpt-oss-120b, which the 5090 cannot. The 5090 has over three times the bandwidth and far more compute, so anything that fits in 32 GB runs about three times faster on it. Choose by the largest model you need.
Which is faster for LLM inference?
The RTX 5090 32GB, by about 213% for token generation on models that fit both (about 202 vs 64 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The Apple M4 Max 128GB, with 96.0 GB usable versus 32.0 GB.