Llama 3.3 70B Instruct needs ~55.3 GB but MacBook Pro M1 Max 32GB only has 23.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
32.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.3 tok/s
TTFT
83502 ms
Safe context
4K
Memory
55.3 GB / 23.0 GB
Offload
60%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 55.3 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.3 tok/s | 45547 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 83502 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 121458 ms | 4K |
| Reasoning | F | Too heavy | 2.3 tok/s | 98684 ms | 4K |
| RAG | F | Too heavy | 2.3 tok/s | 151822 ms | 4K |
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | F0 |
Q3_K_S | 3 | 34.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,199 MSRP
NVIDIA H100 80GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
No, Llama 3.3 70B Instruct requires more memory than MacBook Pro M1 Max 32GB provides.
Llama 3.3 70B Instruct (70B parameters) requires approximately 55.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, Llama 3.3 70B Instruct achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 83502ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B Instruct on MacBook Pro M1 Max 32GB receives a F grade with 2.3 tok/s and 4K context.
On MacBook Pro M1 Max 32GB, Llama 3.3 70B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-m1-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
39.2 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 42.7 GB | Medium | F0 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M1 Max 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
