MPT-30B-Instruct needs ~60.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q5_K_M quantization, expect ~22 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
21.9 tok/s
TTFT
8836 ms
Safe context
8K
Memory
60.1 GB / 92.2 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 | 21.9 tok/s | 4819 ms | 8K |
| Coding | A | Runs well | 21.9 tok/s | 8836 ms | 8K |
| Agentic Coding | B | Tight fit | 21.9 tok/s | 12852 ms | 8K |
| Reasoning | A | Runs well | 21.9 tok/s | 10442 ms | 8K |
| RAG | B | Tight fit | 21.9 tok/s | 16065 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B60 |
Q3_K_S | 3 | 14.7 GB | Low | B61 |
NVFP4 | 4 | 16.8 GB | Medium | B61 |
Q4_K_M | 4 | 18.3 GB | Medium | B61 |
Q5_K_M | 5 | 21.6 GB | High | B62 |
Q6_K | 6 | 24.6 GB | High | B62 |
Q8_0 | 8 | 32.1 GB | Very High | B64 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | B68 |
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 | 6.3 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 70.2 tok/s | |
| Qwen 3.5 122B A10B | 122B | S | 28.9 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 59 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 64.1 tok/s |
Yes, Mac Studio M2 Ultra 128GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 21.9 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 60.1 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 M2 Ultra 128GB, MPT-30B-Instruct achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8836ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on Mac Studio M2 Ultra 128GB receives a A grade with 21.9 tok/s and 8K context.
On Mac Studio M2 Ultra 128GB, 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.
Not always. Mac Studio M2 Ultra 128GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: