MacBook Pro M3 24GBBudget pick
24 GB Unified (+12)
D
Adds memory headroom for longer context windows and future model growth.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
granite embedding 107m multilingual needs ~2.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 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
1.5M
Memory
2.6 GB / 12.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
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 | 763K |
| Coding | D | Runs well | 2.0 tok/s | 96800 ms | 1.5M |
| Agentic Coding | D | Runs well | 2.0 tok/s | 140800 ms | 3.1M |
| Reasoning | D | Runs well | 2.0 tok/s | 114400 ms | 1.5M |
| RAG | D | Runs well | 2.0 tok/s | 176000 ms | 3.1M |
How granite embedding 107m multilingual (0.10700000077486038B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | C46 |
Q3_K_S | 3 | 0.1 GB | Low | C46 |
NVFP4 | 4 | 0.1 GB | Medium | C46 |
Q4_K_M | 4 | 0.1 GB | Medium | C46 |
Q5_K_M | 5 | 0.1 GB | High | C46 |
Q6_K | 6 | 0.1 GB | High | C46 |
Q8_0 | 8 | 0.1 GB | Very High | C46 |
F16Best for your GPU | 16 | 0.2 GB | Maximum | C46 |
Copy-paste commands to run granite embedding 107m multilingual on your machine.
Run
lms load hf-bartowski--granite-embedding-107m-multilingual-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, RTX 3500 Ada Laptop 12GB 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 2.6 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 RTX 3500 Ada Laptop 12GB, 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 RTX 3500 Ada Laptop 12GB receives a D grade with 2.0 tok/s and 1.5M context.
On RTX 3500 Ada Laptop 12GB, granite embedding 107m multilingual can safely use up to 1.5M tokens of context. The model's official context limit is —, 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.
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-rtx-3500-ada-laptop-12gb" 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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