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MPT-30B-Instruct needs ~48.2 GB but RTX A4500 20GB only has 20.0 GB. Try a smaller quantization or lighter model.
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
28.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.5 tok/s
TTFT
54751 ms
Safe context
4K
Memory
48.2 GB / 20.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 48.2 GB, but this setup only exposes 20.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.0 tok/s | 21213 ms | 4K |
| Coding | F | Too heavy | 3.5 tok/s | 54751 ms | 4K |
| Agentic Coding | F | Too heavy | 3.5 tok/s | 79638 ms | 4K |
| Reasoning | F | Too heavy | 3.5 tok/s | 64706 ms | 4K |
| RAG | F | Too heavy | 3.5 tok/s | 99548 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A71 |
Q3_K_SBest for your GPU | 3 | 14.7 GB | Low | A71 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 420%.
~$4,650 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 929%.
~$4,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 500%.
~$5,500 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.
~$40,000 MSRP
| 4 |
16.8 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
