MPT-30B-Instruct needs ~47.8 GB but RTX 5000 Ada Laptop 16GB only has 16.0 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
31.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
65001 ms
Safe context
4K
Memory
47.8 GB / 16.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 47.8 GB, but this setup only exposes 16.0 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 | 3.0 tok/s | 35455 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 65001 ms | 4K |
| Agentic Coding | F | Too heavy | 3.0 tok/s | 94547 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76820 ms | 4K |
| RAG | F | Too heavy | 3.0 tok/s | 118184 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | F0 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 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 |
Opciones de mejora
RTX A6000 48GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 507%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 1100%.
~$4,999 MSRP
NVIDIA L40 48GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 600%.
~$5,500 MSRP
NVIDIA H100 80GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$40,000 MSRP
No, MPT-30B-Instruct requires more memory than RTX 5000 Ada Laptop 16GB provides.
MPT-30B-Instruct (30B parameters) requires approximately 47.8 GB of memory with Q5_K_M quantization.
The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.
On RTX 5000 Ada Laptop 16GB, MPT-30B-Instruct achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 65001ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on RTX 5000 Ada Laptop 16GB receives a F grade with 3.0 tok/s and 4K context.
On RTX 5000 Ada Laptop 16GB, MPT-30B-Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-rtx-5000-ada-laptop-16gb" 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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