MPT-30B-Instruct needs ~60.3 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q5_K_M quantization, expect ~190 tok/s.
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
Fit status
Runs well
Decode
190.4 tok/s
TTFT
1017 ms
Safe context
8K
Memory
60.3 GB / 141.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 190.4 tok/s | 555 ms | 8K |
| Coding | A | Runs well | 190.4 tok/s | 1017 ms | 8K |
| Agentic Coding | A | Runs well | 190.4 tok/s | 1479 ms | 8K |
| Reasoning | A | Runs well | 190.4 tok/s | 1202 ms | 8K |
| RAG | A | Runs well | 190.4 tok/s | 1849 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B59 |
Q3_K_S | 3 | 14.7 GB | Low | B59 |
NVFP4 | 4 | 16.8 GB | Medium | B59 |
Q4_K_M | 4 | 18.3 GB | Medium | B59 |
Q5_K_M | 5 | 21.6 GB | High | B59 |
Q6_K | 6 | 24.6 GB | High | B60 |
Q8_0 | 8 | 32.1 GB | Very High | B61 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | B65 |
Copy-paste commands to run MPT-30B-Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mosaicml/mpt-30b-instruct" \
--hf-file "mpt-30b-instruct-Q5_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 58.4 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 609.7 tok/s | |
| Qwen 3.5 122B A10B | 122B | S | 162.1 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 512.4 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 557.2 tok/s |
Yes, NVIDIA H200 PCIe 141GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 190.4 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 60.3 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 NVIDIA H200 PCIe 141GB, MPT-30B-Instruct achieves approximately 190.4 tokens per second decode speed with a time-to-first-token of 1017ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on NVIDIA H200 PCIe 141GB receives a A grade with 190.4 tok/s and 8K context.
On NVIDIA H200 PCIe 141GB, MPT-30B-Instruct can safely use up to 8K 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-h200-pcie-141gb" 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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