MPT-30B-Instruct needs ~48.6 GB but RTX 4090 24GB only has 24.0 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
24.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.1 tok/s
TTFT
31561 ms
Safe context
4K
Memory
48.6 GB / 24.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 48.6 GB, but this setup only exposes 24.0 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 | 11.0 tok/s | 9636 ms | 4K |
| Coding | F | Too heavy | 6.1 tok/s | 31561 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 51894 ms | 4K |
| Reasoning | F | Too heavy | 6.1 tok/s | 37300 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 64868 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A71 |
Q3_K_S | 3 | 14.7 GB | Low | A70 |
NVFP4 | 4 |
Upgrade options
RTX A6000 48GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 198%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 490%.
~$4,999 MSRP
Quadro RTX 8000 48GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 130%.
~$5,800 MSRP
NVIDIA H100 80GBBiggest leapMakes 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
No, MPT-30B-Instruct requires more memory than RTX 4090 24GB provides.
MPT-30B-Instruct (30B parameters) requires approximately 48.6 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 4090 24GB, MPT-30B-Instruct achieves approximately 6.1 tokens per second decode speed with a time-to-first-token of 31561ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on RTX 4090 24GB receives a F grade with 6.1 tok/s and 4K context.
On RTX 4090 24GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
16.8 GB |
| Medium |
| A70 |
Q4_K_MBest for your GPU | 4 | 18.3 GB | Medium | B70 |
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.
