Llama 3.1 70B needs ~58.9 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~15 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
15.3 tok/s
TTFT
12657 ms
Safe context
50K
Memory
58.9 GB / 69.1 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 15.3 tok/s | 6904 ms | 50K |
| Coding | A | Tight fit | 15.3 tok/s | 12657 ms | 50K |
| Agentic Coding | A | Tight fit | 15.3 tok/s | 18410 ms | 50K |
| Reasoning | A | Tight fit | 15.3 tok/s | 14958 ms | 50K |
| RAG | A | Tight fit | 15.3 tok/s | 23013 ms | 50K |
How Llama 3.1 70B (70B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | A76 |
Q3_K_S | 3 | 34.3 GB | Low | A78 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.1 70B on your machine.
Run
ollama run llama3.1Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Command A 111B | 111B | A | 7.4 tok/s | |
 Qwen 2.5 VL 72B | 72B | S | 14.9 tok/s |
Yes, MacBook Pro M4 Max 96GB can run Llama 3.1 70B with a A grade (Tight fit). Expected decode speed: 15.3 tok/s.
Llama 3.1 70B (70B parameters) requires approximately 58.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 96GB, Llama 3.1 70B achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12657ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 70B on MacBook Pro M4 Max 96GB receives a A grade with 15.3 tok/s and 50K context.
On MacBook Pro M4 Max 96GB, Llama 3.1 70B can safely use up to 50K tokens of context. The model's official context limit is 128K, 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/llama-3.1-70b-on-m4-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:
39.2 GB |
| Medium |
| A79 |
Q4_K_M | 4 | 42.7 GB | Medium | A79 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | A79 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
| Qwen3-Coder-Next | 80B | S | 23.2 tok/s |
Not always. MacBook Pro M4 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.