RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+1392)
A
Raises estimated decode speed by about 529%.71.1 tok/s decode
Raises estimated decode speed by about 529%.
~$9,999 MSRP
MPT-30B-Instruct needs ~60.1 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q5_K_M quantization, expect ~11 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
11.3 tok/s
TTFT
17082 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 | 11.3 tok/s | 9318 ms | 8K |
| Coding | B | Runs well | 11.3 tok/s | 17082 ms | 8K |
| Agentic Coding | B | Tight fit | 11.3 tok/s | 24847 ms | 8K |
| Reasoning | B | Runs well | 11.3 tok/s | 20188 ms | 8K |
| RAG | B | Tight fit | 11.3 tok/s | 31059 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M3 Max 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 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 529%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 460%.
~$9,999 MSRP
Yes, MacBook Pro M3 Max 128GB can run MPT-30B-Instruct with a B grade (Runs well). Expected decode speed: 11.3 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 MacBook Pro M3 Max 128GB, MPT-30B-Instruct achieves approximately 11.3 tokens per second decode speed with a time-to-first-token of 17082ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on MacBook Pro M3 Max 128GB receives a B grade with 11.3 tok/s and 8K context.
On MacBook Pro M3 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-m3-max-128gb" 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 |
| 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 |
Not always. MacBook Pro M3 Max 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.