MPT-30B-Instruct needs ~56.6 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 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
11.0 tok/s
TTFT
17671 ms
Safe context
8K
Memory
56.6 GB / 69.1 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 | 11.0 tok/s | 9639 ms | 8K |
| Coding | A | Runs well | 11.0 tok/s | 17671 ms | 8K |
| Agentic Coding | B | Very compromised (needs ~2.9 GB host RAM) | 8.7 tok/s | 32261 ms | 8K |
| Reasoning | A | Runs well | 11.0 tok/s | 20884 ms | 8K |
| RAG | B | Very compromised (needs ~2.9 GB host RAM) | 8.7 tok/s | 40326 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B62 |
Q3_K_S | 3 | 14.7 GB | Low | B62 |
NVFP4 | 4 | 16.8 GB | Medium | B63 |
Q4_K_M | 4 | 18.3 GB | Medium | B63 |
Q5_K_M | 5 | 21.6 GB | High | B64 |
Q6_K | 6 | 24.6 GB | High | B65 |
Q8_0Best for your GPU | 8 | 32.1 GB | Very High | B66 |
F16 | 16 | 61.5 GB | Maximum | F0 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 35.1 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 32.4 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 35.3 tok/s | |
| Qwen 3 32B | 32B | S | 12.9 tok/s | |
| Qwen 3 30B A3B | 30.5B | S | 35.1 tok/s |
Yes, MacBook Pro M2 Max 96GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 11.0 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 56.6 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 M2 Max 96GB, MPT-30B-Instruct achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17671ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on MacBook Pro M2 Max 96GB receives a A grade with 11.0 tok/s and 8K context.
On MacBook Pro M2 Max 96GB, 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. MacBook Pro M2 Max 96GB 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-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: