MPT-30B-Instruct needs ~49.4 GB but Radeon Pro W7800 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
4.8 tok/s
TTFT
40186 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) | 8.5 tok/s | 12402 ms | 4K |
| Coding | F | Too heavy | 4.8 tok/s | 40186 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116982 ms | 4K |
| Reasoning | F | Too heavy | 4.8 tok/s | 47492 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146228 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for MPT-30B-Instruct at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~28 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 28.4 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 26.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 22.7 | Heavy offload |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 21.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 20.8 | Fits |
 RTX 5090 32GB | 32 GB | Q5_K_M | 17.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 10.5 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 9.1 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 8.3 | Heavy offload |
| RTX 4090 24GB | 24 GB | Q5_K_M | 6.1 | Too big |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 5.5 | Too big |
| RTX 3090 24GB | 24 GB | Q5_K_M | 5.2 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 4.3 | Too big |
| RTX 4070 12GB | 12 GB | Q5_K_M | 2.7 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How MPT-30B-Instruct (30B params) fits at each quantization level on Radeon Pro W7800 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 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 231%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 231%.
~$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 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 Radeon Pro W7800 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 Pro W7800 32GB, MPT-30B-Instruct achieves approximately 4.8 tokens per second decode speed with a time-to-first-token of 40186ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on Radeon Pro W7800 32GB receives a F grade with 4.8 tok/s and 4K context.
On Radeon Pro W7800 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-pro-w7800-32gb" 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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