MosaicML
MPT-30B-Instruct (30B parameters) requires approximately 46.8 GB of VRAM with Q5_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 54 GB of VRAM.
Get started
— copy & paste to run locallyCopy-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 99Quick specs
About this model
Related models
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.
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Quantization
How much VRAM MPT-30B-Instruct (30B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q5_K_M uses ~21.6 GB — about 33% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 11.7 GB | Low | Too big |
| Q3_K_S | 3 | 14.7 GB | Low | Too big |
| NVFP4 | 4 | 16.8 GB | Medium | Too big |
| Q4_K_M | 4 | 18.3 GB | Medium | Too big |
| Q5_K_Mrecommended | 5 | 21.6 GB | High | Too big |
| Q6_K | 6 | 24.6 GB | High | Too big |
| Q8_0 | 8 | 32.1 GB | Very High | Too big |
| F16 | 16 | 61.5 GB | Maximum | Too big |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
MPT-30B-Instruct (30B parameters) requires approximately 46.8 GB of VRAM with Q5_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, MacBook Pro M4 Max 96GB can run MPT-30B-Instruct with a compatibility score of 74/100. It provides 96 GB of memory and achieves approximately 28.4 tokens per second.
The recommended quantization for MPT-30B-Instruct is Q5_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for MPT-30B-Instruct: NVIDIA H100 80GB (score: 77/100), NVIDIA H800 80GB (score: 77/100), NVIDIA A100 80GB (score: 77/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, MPT-30B-Instruct is well-suited for chat as well as reasoning. It was designed with these use cases in mind.
See also