MacBook Pro 14/16 (2024) M4 Max 128GB vs RTX 4090 24GB for local LLMs
Memory fit, speed and context compared with the same math as the advisor. MacBook Pro 14/16 (2024) M4 Max 128GB has 66.1 GB more usable memory (89.1 vs 23.0 GB) and higher bandwidth than the other (546 vs 1008 GB/s).
Verdict from the numbers
- Fit. MacBook Pro 14/16 (2024) M4 Max 128GB fits 13 of 13 models fully at 16k context (largest: gpt-oss 120B Q8_0); RTX 4090 24GB fits 11 (largest: Qwen3.6 35B-A3B UD-Q4_K_M). Only MacBook Pro 14/16 (2024) M4 Max 128GB fits fully: gpt-oss 120B, GLM-4.5-Air.
- Speed. On the 11 models that fit fully on both, RTX 4090 24GB is faster by a typical 3.1x. MacBook Pro 14/16 (2024) M4 Max 128GB is ahead on 0, RTX 4090 24GB on 11. No model is measured on both machines, so every comparison involves an estimate; read it as a guide.
- Trade-off. MacBook Pro 14/16 (2024) M4 Max 128GB has 66.1 GB more usable memory (89.1 vs 23.0 GB) and higher bandwidth than the other (546 vs 1008 GB/s).
Where to check prices
Price tier note
We do not list prices because they move daily and differ between new and used stock. What the data does show is what the memory difference buys: MacBook Pro 14/16 (2024) M4 Max 128GB has 66.1 GB more usable memory, which moves 2 of 13 models into full-GPU fit (gpt-oss 120B, GLM-4.5-Air). Use the Amazon links above to compare current listings and decide whether the difference is worth it for the models you want to run.
Model by model · 16k context, f16 KV cache
| Model | MacBook Pro 14/16 (2024) M4 Max 128GB fit | tok/s | RTX 4090 24GB fit | tok/s | Faster / fits |
|---|---|---|---|---|---|
| Gemma 4 26B-A4B Q8_0 | fits fully | 50 estimated | fits fully | 135 scaled from measured | RTX 4090 24GB |
| Gemma 4 31B Q4_K_M | fits fully | 14 estimated | fits fully | 44 measured | RTX 4090 24GB |
| Gemma 4 12B Q8_0 | fits fully | 20 scaled from measured | fits fully | 64 measured | RTX 4090 24GB |
| Qwen3.6 27B Q4_K_M | fits fully | 15 estimated | fits fully | 42 scaled from measured | RTX 4090 24GB |
| Qwen3.6 35B-A3B Q8_0 | fits fully | 66 estimated | fits fully | 190 measured | RTX 4090 24GB |
| Qwen3.5 9B Q8_0 | fits fully | 27 scaled from measured | fits fully | 95 measured | RTX 4090 24GB |
| Qwen3.5 4B Q8_0 | fits fully | 57 scaled from measured | fits fully | 158 measured | RTX 4090 24GB |
| gpt-oss 20B Q8_0 | fits fully | 97 estimated | fits fully | 258 measured | RTX 4090 24GB |
| gpt-oss 120B Q8_0 | fits fully | 73 estimated | does not fit | - | MacBook Pro 14/16 (2024) M4 Max 128GB (fits) |
| Qwen3-Coder 30B-A3B Q8_0 | fits fully | 58 estimated | fits fully | 266 measured | RTX 4090 24GB |
| Mistral Small 3.2 24B Q5_K_M | fits fully | 15 estimated | fits fully | 42 scaled from measured | RTX 4090 24GB |
| Qwen3-VL 8B Q8_0 | fits fully | 30 scaled from measured | fits fully | 102 scaled from measured | RTX 4090 24GB |
| GLM-4.5-Air Q5_K_M | fits fully | 22 estimated | does not fit | - | MacBook Pro 14/16 (2024) M4 Max 128GB (fits) |
Badges: measured medians of real llama.cpp runs; scaled from measured and estimated come from our bandwidth model. See sources and methods.
How much context fits?
Largest context where weights plus KV cache stay inside usable memory, per model that fits fully on at least one side. q8_0 KV roughly halves the cache.
| Model | MacBook Pro 14/16 (2024) M4 Max 128GB f16 | RTX 4090 24GB f16 | MacBook Pro 14/16 (2024) M4 Max 128GB q8_0 | RTX 4090 24GB q8_0 |
|---|---|---|---|---|
| gpt-oss 120B | 128k | - | 128k | - |
| GLM-4.5-Air | 16k | - | 32k | - |
| Qwen3.6 35B-A3B | 1M | 32k | 1M | 64k |
| Gemma 4 31B | 256k | 32k | 256k | 64k |
| Qwen3-Coder 30B-A3B | 512k | 32k | 1M | 64k |
| Qwen3.6 27B | 1M | 32k | 1M | 64k |
| Gemma 4 26B-A4B | 256k | 64k | 256k | 128k |
| Mistral Small 3.2 24B | 128k | 16k | 128k | 32k |
| gpt-oss 20B | 128k | 128k | 128k | 128k |
| Gemma 4 12B | 256k | 256k | 256k | 256k |
| Qwen3.5 9B | 1M | 256k | 1M | 512k |
| Qwen3-VL 8B | 512k | 64k | 1M | 128k |
| Qwen3.5 4B | 1M | 512k | 1M | 1M |
Spec summary
| MacBook Pro 14/16 (2024) M4 Max 128GB | RTX 4090 24GB | |
|---|---|---|
| Usable memory for model + KV cache | 89.1 GB | 23.0 GB |
| Memory bandwidth | 546 GB/s | 1008 GB/s |
| Models that fit fully at 16k | 13 of 13 | 11 of 13 |
| Models that need offload or do not fit | 0 | 2 |
| Measured data points on file | 4 | 24 |
FAQ
Which is faster for local LLMs, MacBook Pro 14/16 (2024) M4 Max 128GB or RTX 4090 24GB?
On the 11 models that fit fully on both, RTX 4090 24GB is typically 3.1x faster (geometric mean). MacBook Pro 14/16 (2024) M4 Max 128GB wins 0 and RTX 4090 24GB wins 11. 0 comparisons are measured on both.
Which fits more models, MacBook Pro 14/16 (2024) M4 Max 128GB or RTX 4090 24GB?
MacBook Pro 14/16 (2024) M4 Max 128GB fits 13 of 13 models fully at 16k context and RTX 4090 24GB fits 11. Only MacBook Pro 14/16 (2024) M4 Max 128GB: gpt-oss 120B, GLM-4.5-Air.
Can I run long context on MacBook Pro 14/16 (2024) M4 Max 128GB or RTX 4090 24GB?
At f16 KV cache, the largest-model context limits are in the table above (for example gpt-oss 120B on MacBook Pro 14/16 (2024) M4 Max 128GB: 128k; Qwen3.6 35B-A3B on RTX 4090 24GB: 32k). A q8_0 KV cache extends it further.
Do you state prices for MacBook Pro 14/16 (2024) M4 Max 128GB or RTX 4090 24GB?
No. Prices change daily, so we link to Amazon search results instead and focus on measured and estimated speed and memory fit.
Related
MacBook Pro 14/16 (2024) M4 Max 128GB · RTX 4090 24GB · Measured model pages: gpt-oss 20B · Qwen3.6 35B-A3B · Qwen3-Coder 30B-A3B · Gemma 4 31B · Qwen3.6 27B · Gemma 4 26B-A4B
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