MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.5.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Llama 3.3 70B Instruct needs ~62.2 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~5 tok/s.
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
Fit status
Tight fit
Decode
5.4 tok/s
TTFT
35632 ms
Safe context
30K
Memory
62.2 GB / 69.1 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 5.4 tok/s | 19436 ms | 30K |
| Coding | C | Tight fit | 5.4 tok/s | 35632 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.8 GB host RAM) | 5.2 tok/s | 53982 ms | 30K |
| Reasoning | C | Tight fit | 5.4 tok/s | 42111 ms | 30K |
| RAG | C | Runs with offload (needs ~0.8 GB host RAM) | 5.2 tok/s | 67477 ms |
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C45 |
Q3_K_S | 3 | 34.3 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.3 70B Instruct on your machine.
Run
lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 102%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
NVIDIA H100 80GBBiggest leapRaises estimated decode speed by about 1120%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$40,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run Llama 3.3 70B Instruct with a C grade (Tight fit). Expected decode speed: 5.4 tok/s.
Llama 3.3 70B Instruct (70B parameters) requires approximately 62.2 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 M2 Max 96GB, Llama 3.3 70B Instruct achieves approximately 5.4 tokens per second decode speed with a time-to-first-token of 35632ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B Instruct on MacBook Pro M2 Max 96GB receives a C grade with 5.4 tok/s and 30K context.
On MacBook Pro M2 Max 96GB, Llama 3.3 70B Instruct can safely use up to 30K 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-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 30K |
39.2 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 42.7 GB | Medium | C48 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | C48 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M2 Max 96GB 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.