MacBook Pro M4 16GBBudget pick
16 GB Unified (+6)
B
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 tok/s decode
~$599 MSRP
All MiniLM L6 v2 needs ~2.5 GB VRAM. RTX 3080 10GB has 10.0 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
2.5 GB / 10.0 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 | 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 RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | A74 |
Q3_K_S | 3 | 0.0 GB | Low | A74 |
NVFP4 | 4 |
Copy-paste commands to run All MiniLM L6 v2 on your machine.
Run
ollama run all-minilmUpgrade options
~$599 MSRP
~$999 MSRP
Yes, RTX 3080 10GB 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 2.5 GB of memory with F16 quantization.
The recommended quantization for All MiniLM L6 v2 is F16, which balances quality and memory efficiency.
On RTX 3080 10GB, 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 RTX 3080 10GB receives a B grade with 2.0 tok/s and 256 context.
On RTX 3080 10GB, 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-rtx-3080-10gb" 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 |
| A74 |
Q4_K_M | 4 | 0.0 GB | Medium | A74 |
Q5_K_M | 5 | 0.0 GB | High | A74 |
Q6_K | 6 | 0.0 GB | High | A74 |
Q8_0 | 8 | 0.0 GB | Very High | A74 |
F16Best for your GPU | 16 | 0.0 GB | Maximum | A74 |
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.