granite embedding 107m multilingual needs ~4.5 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M 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
3.0M
Memory
4.5 GB / 23.0 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 | D | Runs well | 2.0 tok/s | 52800 ms | 1.5M |
| Coding | D | Runs well | 2.0 tok/s | 96800 ms | 3.0M |
| Agentic Coding | D | Runs well | 2.0 tok/s | 140800 ms | 6.0M |
| Reasoning | D | Runs well | 2.0 tok/s | 114400 ms | 3.0M |
| RAG | D | Runs well | 2.0 tok/s | 176000 ms | 6.0M |
How granite embedding 107m multilingual (0.10700000077486038B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | C44 |
Q3_K_S | 3 | 0.1 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run granite embedding 107m multilingual on your machine.
Run
lms load hf-bartowski--granite-embedding-107m-multilingual-gguf && lms server startYes, MacBook Pro M2 Pro 32GB can run granite embedding 107m multilingual with a D grade (Runs well). Expected decode speed: 2.0 tok/s.
granite embedding 107m multilingual (0.10700000077486038B parameters) requires approximately 4.5 GB of memory with Q4_K_M quantization.
The recommended quantization for granite embedding 107m multilingual is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, granite embedding 107m multilingual achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, granite embedding 107m multilingual on MacBook Pro M2 Pro 32GB receives a D grade with 2.0 tok/s and 3.0M context.
On MacBook Pro M2 Pro 32GB, granite embedding 107m multilingual can safely use up to 3.0M tokens of context. The model's official context limit is —, 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/hf-bartowski--granite-embedding-107m-multilingual-gguf-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.1 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 0.1 GB | Medium | C44 |
Q5_K_M | 5 | 0.1 GB | High | C44 |
Q6_K | 6 | 0.1 GB | High | C44 |
Q8_0 | 8 | 0.1 GB | Very High | C44 |
F16Best for your GPU | 16 | 0.2 GB | Maximum | C44 |
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 Pro 32GB 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.