Llama 3.1 70B needs ~58.9 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~6 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
5.9 tok/s
TTFT
32765 ms
Safe context
50K
Memory
58.9 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 | A | Runs well | 5.9 tok/s | 17872 ms | 50K |
| Coding | A | Tight fit | 5.9 tok/s | 32765 ms | 50K |
| Agentic Coding | A | Tight fit | 5.9 tok/s | 47659 ms | 50K |
| Reasoning | A | Tight fit | 5.9 tok/s | 38723 ms | 50K |
| RAG | A | Tight fit | 5.9 tok/s | 59574 ms | 50K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 3.1 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 18.0 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 15.3 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 14.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 11.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 11.6 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 11.2 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 9.5 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 8.7 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 8.7 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.4 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.6 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.3 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 3.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.5 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.3 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 3.1 70B (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 | A76 |
Q3_K_S | 3 | 34.3 GB | Low | A78 |
NVFP4 | 4 | 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 |
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 | B | 2.9 tok/s | |
 Qwen 2.5 VL 72B | 72B | S | 5.7 tok/s | |
| Qwen3-Coder-Next | 80B | S | 17.2 tok/s |
Yes, MacBook Pro M2 Max 96GB can run Llama 3.1 70B with a A grade (Tight fit). Expected decode speed: 5.9 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 M2 Max 96GB, Llama 3.1 70B achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32765ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 70B on MacBook Pro M2 Max 96GB receives a A grade with 5.9 tok/s and 50K context.
On MacBook Pro M2 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.1-70b-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>
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