MPT-30B-Instruct needs ~73.9 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q5_K_M quantization, expect ~26 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
26.3 tok/s
TTFT
7361 ms
Safe context
8K
Memory
73.9 GB / 184.3 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 26.3 tok/s | 4015 ms | 8K |
| Coding | B | Runs well | 26.3 tok/s | 7361 ms | 8K |
| Agentic Coding | B | Runs well | 26.3 tok/s | 10708 ms | 8K |
| Reasoning | B | Runs well | 26.3 tok/s | 8700 ms | 8K |
| RAG | B | Runs well | 26.3 tok/s | 13385 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 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 |
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
Raises estimated decode speed by about 949%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Raises estimated decode speed by about 642%.
~$15,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run MPT-30B-Instruct with a B grade (Runs well). Expected decode speed: 26.3 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 73.9 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 Mac Studio M3 Ultra 256GB, MPT-30B-Instruct achieves approximately 26.3 tokens per second decode speed with a time-to-first-token of 7361ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on Mac Studio M3 Ultra 256GB receives a B grade with 26.3 tok/s and 8K context.
On Mac Studio M3 Ultra 256GB, 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-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.