MPT-30B-Instruct needs ~49.4 GB but Radeon AI PRO R9700 32GB only has 32.0 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
17.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.6 tok/s
TTFT
34483 ms
Safe context
4K
Memory
49.4 GB / 32.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 49.4 GB, but this setup only exposes 32.0 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 | C | Very compromised (needs ~3.3 GB host RAM) | 9.7 tok/s | 10831 ms | 4K |
| Coding | F | Too heavy | 5.6 tok/s | 34483 ms | 4K |
| Agentic Coding | F | Too heavy | 2.7 tok/s | 105284 ms | 4K |
| Reasoning | F | Too heavy | 5.6 tok/s | 40752 ms | 4K |
| RAG | F | Too heavy | 2.7 tok/s | 131605 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B68 |
Q3_K_S | 3 | 14.7 GB | Low | B69 |
NVFP4 | 4 | 16.8 GB | Medium | A70 |
Q4_K_M | 4 | 18.3 GB | Medium | B70 |
Q5_K_M | 5 | 21.6 GB | High | B70 |
Q6_KBest for your GPU | 6 | 24.6 GB | High | B69 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 184%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 184%.
~$3,999 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.
~$8,000 MSRP
NVIDIA H100 80GB最大飞跃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
No, MPT-30B-Instruct requires more memory than Radeon AI PRO R9700 32GB provides.
MPT-30B-Instruct (30B parameters) requires approximately 49.4 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 Radeon AI PRO R9700 32GB, MPT-30B-Instruct achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34483ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on Radeon AI PRO R9700 32GB receives a F grade with 5.6 tok/s and 4K context.
On Radeon AI PRO R9700 32GB, 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-radeon-ai-pro-r9700-32gb" 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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