All MiniLM L6 v2 needs ~4.1 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With F16 quantization, expect ~2 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
Runs well
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
256
Memory
4.1 GB / 17.3 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 | B | Runs well | 2.0 tok/s | 52800 ms | 256 |
| Coding | B | Runs well | 2.0 tok/s | 96800 ms | 256 |
| Agentic Coding | B | Runs well | 2.0 tok/s | 140800 ms | 256 |
| Reasoning | B | Runs well | 2.0 tok/s | 114400 ms | 256 |
| RAG | B | Runs well | 2.0 tok/s | 176000 ms | 256 |
How All MiniLM L6 v2 (0.023000000044703484B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | A72 |
Q3_K_S | 3 | 0.0 GB | Low | A72 |
NVFP4 | 4 |
Copy-paste commands to run All MiniLM L6 v2 on your machine.
Run
ollama run all-minilmYes, MacBook Pro M3 24GB can run All MiniLM L6 v2 with a B grade (Runs well). Expected decode speed: 2.0 tok/s.
All MiniLM L6 v2 (0.023000000044703484B parameters) requires approximately 4.1 GB of memory with F16 quantization.
The recommended quantization for All MiniLM L6 v2 is F16, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, All MiniLM L6 v2 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.
For coding workloads, All MiniLM L6 v2 on MacBook Pro M3 24GB receives a B grade with 2.0 tok/s and 256 context.
On MacBook Pro M3 24GB, All MiniLM L6 v2 can safely use up to 256 tokens of context. The model's official context limit is 256, 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/all-minilm-l6-v2-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.0 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 0.0 GB | Medium | A72 |
Q5_K_M | 5 | 0.0 GB | High | A72 |
Q6_K | 6 | 0.0 GB | High | A72 |
Q8_0 | 8 | 0.0 GB | Very High | A72 |
F16Best for your GPU | 16 | 0.0 GB | Maximum | A72 |
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 M3 24GB 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.