MPT-30B-Instruct needs ~64.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q5_K_M quantization, expect ~317 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
317.3 tok/s
TTFT
610 ms
Safe context
8K
Memory
64.2 GB / 180.0 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 | 317.3 tok/s | 350 ms | 8K |
| Coding | A | Runs well | 317.3 tok/s | 610 ms | 8K |
| Agentic Coding | A | Runs well | 317.3 tok/s | 887 ms | 8K |
| Reasoning | A | Runs well | 317.3 tok/s | 721 ms | 8K |
| RAG | A | Runs well | 317.3 tok/s | 1109 ms | 8K |
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 NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B58 |
Q3_K_S | 3 | 14.7 GB | Low | B58 |
NVFP4 | 4 | 16.8 GB | Medium | B58 |
Q4_K_M | 4 | 18.3 GB | Medium | B58 |
Q5_K_M | 5 | 21.6 GB | High | B58 |
Q6_K | 6 | 24.6 GB | High | B59 |
Q8_0 | 8 | 32.1 GB | Very High | B60 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | B63 |
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 | 97.4 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 1016.1 tok/s | |
| Qwen 3.5 122B A10B | 122B | S | 270.2 tok/s | |
 DeepSeek V4 Flash | 284B | S | 144.8 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 854 tok/s |
Yes, NVIDIA B200 180GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 317.3 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 64.2 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 NVIDIA B200 180GB, MPT-30B-Instruct achieves approximately 317.3 tokens per second decode speed with a time-to-first-token of 610ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on NVIDIA B200 180GB receives a A grade with 317.3 tok/s and 8K context.
On NVIDIA B200 180GB, 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-b200-180gb" 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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