Llama 3.3 70B needs ~51.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With NVFP4 quantization, expect ~4 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
9.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
64330 ms
Safe context
4K
Memory
55.4 GB / 46.1 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 4.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~5.5 GB host RAM) | 3.2 tok/s | 33130 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 64330 ms | 4K |
| Agentic Coding | F | Too heavy | 2.7 tok/s | 103717 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76027 ms | 4K |
| RAG | F | Too heavy | 2.7 tok/s | 129646 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 3.3 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 3.3 70B (70B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | A82 |
Q3_K_SBest for your GPU | 3 | 34.3 GB | Low | A82 |
NVFP4 | 4 | 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 |
Copy-paste commands to run Llama 3.3 70B on your machine.
Run
ollama run llama3.3Upgrade-Optionen
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.
ca. $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.
ca. $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.
ca. $3,999 MSRP
NVIDIA H100 80GBGrößter SprungMakes 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.
ca. $40,000 MSRP
Yes, Mac mini M4 64GB can run Llama 3.3 70B at NVFP4 quantization (Very compromised (needs ~4.4 GB host RAM)). The recommended Q4_K_M requires 55.4 GB which exceeds available memory, but at NVFP4 it needs only 51.9 GB. Expected decode speed: 3.7 tok/s.
Llama 3.3 70B (70B parameters) requires approximately 55.4 GB at Q4_K_M quantization. On Mac mini M4 64GB, it fits at NVFP4 using 51.9 GB.
The recommended quantization is Q4_K_M, but on Mac mini M4 64GB the best fitting quantization is NVFP4, which uses 51.9 GB.
On Mac mini M4 64GB, Llama 3.3 70B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 51712ms using NVFP4 quantization.
For coding workloads, Llama 3.3 70B on Mac mini M4 64GB receives a F grade with 3.0 tok/s and 4K context.
On Mac mini M4 64GB, Llama 3.3 70B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. Mac mini M4 64GB 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.
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<iframe src="https://willitrunai.com/embed/llama-3.3-70b-on-m4-mini-64gb" 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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