RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+1392)
A
Raises estimated decode speed by about 529%.71.1 tok/s decodificação
Raises estimated decode speed by about 529%.
~$9,999 MSRP
Can MPT-30B-Instruct run on MacBook Pro M3 Max 128GB?
YES — Runs Great
B70Good
Estimated from fit model
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.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 60.1 GB, 11.3 tok/s, Runs well
60.1 GB required92.2 GB available
65% VRAM used
Fit status
Runs well
Decode
11.3 tok/s
TTFT
17082 ms
Safe context
8K
Memory
60.1 GB / 92.2 GB
Memory breakdown
Weights21.6 GB
KV Cache23.4 GB
Runtime1.2 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsMPT-30B-Instruct on MacBook Pro M3 Max 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 11.3 tok/s decode · 17.1s TTFT (warm) · 28 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| 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 |
Quantization options
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 | 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 |
Get started
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 99Opções de upgrade
Hardware que roda bem MPT-30B-Instruct
Frequently asked questions
Can MacBook Pro M3 Max 128GB run MPT-30B-Instruct?
Yes, MacBook Pro M3 Max 128GB can run MPT-30B-Instruct with a B grade (Runs well). Expected decode speed: 11.3 tok/s.
How much VRAM does MPT-30B-Instruct need?
MPT-30B-Instruct (30B parameters) requires approximately 60.1 GB of memory with Q5_K_M quantization.
What is the best quantization for MPT-30B-Instruct?
The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.
What speed will MPT-30B-Instruct run at on MacBook Pro M3 Max 128GB?
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.
Can MacBook Pro M3 Max 128GB run MPT-30B-Instruct for coding?
For coding workloads, MPT-30B-Instruct on MacBook Pro M3 Max 128GB receives a B grade with 11.3 tok/s and 8K context.
What context window can MPT-30B-Instruct use on MacBook Pro M3 Max 128GB?
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.
Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for MPT-30B-Instruct?
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.
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