1792 GB/s (+1392)
C
Raises estimated decode speed by about 530%.35.3 tok/s decode
Raises estimated decode speed by about 530%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
Can Llama 3.3 70B Instruct run on MacBook Pro M3 Max 128GB?
YES — Runs Great
C48Usable
○Estimated from fit model
Llama 3.3 70B Instruct needs ~65.6 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~6 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Memory bandwidth
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 65.6 GB, 5.6 tok/s, Runs well
65.6 GB required92.2 GB available
71% VRAM used
Fit status
Runs well
Decode
5.6 tok/s
TTFT
34445 ms
Safe context
68K
Memory
65.6 GB / 92.2 GB
Memory breakdown
Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsLlama 3.3 70B Instruct on MacBook Pro M3 Max 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 5.6 tok/s decode · 34.4s TTFT (warm) · 14 tok/s prefill
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 5.6 tok/s | 18788 ms | 68K |
| Coding | C | Runs well | 5.6 tok/s | 34445 ms | 68K |
| Agentic Coding | C | Runs well | 5.6 tok/s | 50101 ms | 68K |
| Reasoning | C | Runs well | 5.6 tok/s | 40707 ms | 68K |
| RAG | C | Runs well | 5.6 tok/s | 62627 ms | 68K |
Quantization options
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C43 |
Q3_K_S | 3 | 34.3 GB | Low | C44 |
NVFP4 | 4 | 39.2 GB | Medium | C45 |
Q4_K_M | 4 | 42.7 GB | Medium | C46 |
Q5_K_M | 5 | 50.4 GB | High | C48 |
Q6_K | 6 | 57.4 GB | High | C48 |
Q8_0Best for your GPU | 8 | 74.9 GB | Very High | C48 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Get started
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