MPT-30B-Instruct needs ~48.2 GB but RX 7900 XT 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
3.4 tok/s
TTFT
56941 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 | 4.8 tok/s | 22062 ms | 4K |
| Coding | F | Too heavy | 3.4 tok/s | 56941 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 82824 ms | 4K |
| Reasoning | F | Too heavy | 3.4 tok/s | 67294 ms | 4K |
| RAG | F | Too heavy | 3.4 tok/s | 103529 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on RX 7900 XT 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 |
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 |
アップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 368%.
〜$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 368%.
〜$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最大の飛躍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 RX 7900 XT 20GB provides.
MPT-30B-Instruct (30B parameters) requires approximately 48.2 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 RX 7900 XT 20GB, MPT-30B-Instruct achieves approximately 3.4 tokens per second decode speed with a time-to-first-token of 56941ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on RX 7900 XT 20GB receives a F grade with 3.4 tok/s and 4K context.
On RX 7900 XT 20GB, 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-rx-7900-xt-20gb" 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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