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MPT-30B-Instruct needs ~53.1 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q5_K_M quantization, expect ~13 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
7.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.9 GB host RAM)
Decode
22.7 tok/s
TTFT
8523 ms
Safe context
8K
Memory
53.1 GB / 46.1 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 16.2 tok/s | 6501 ms | 8K |
| Coding | B | Very compromised | 13.0 tok/s | 14882 ms | 8K |
| Agentic Coding | F | Too heavy | 8.5 tok/s | 33265 ms | 8K |
| Reasoning | B | Very compromised | 13.0 tok/s | 17588 ms | 8K |
| RAG | F | Too heavy | 8.5 tok/s | 41581 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B64 |
Q3_K_S | 3 | 14.7 GB | Low | B65 |
NVFP4 | 4 |
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 99Upgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 25%.
~$2,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$3,199 MSRP
16.8 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 18.3 GB | Medium | B67 |
Q5_K_M | 5 | 21.6 GB | High | B68 |
Q6_K | 6 | 24.6 GB | High | B69 |
Q8_0Best for your GPU | 8 | 32.1 GB | Very High | B68 |
F16 | 16 | 61.5 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.