96 GB VRAM (+32)1792 GB/s (+1192)
A
Raises estimated decode speed by about 222%.71.1 tok/s decode
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Can MPT-30B-Instruct run on NVIDIA A16 64GB?
YES — Tight Fit
B70Good
○Estimated from fit model
MPT-30B-Instruct needs ~52.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~22 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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) — 52.6 GB, 22.1 tok/s, Tight fit
52.6 GB required64.0 GB available
82% VRAM used
Fit status
Tight fit
Decode
22.1 tok/s
TTFT
8760 ms
Safe context
8K
Memory
52.6 GB / 64.0 GB
Memory breakdown
Weights21.6 GB
KV Cache23.4 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsMPT-30B-Instruct on NVIDIA A16 64GB
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: 22.1 tok/s decode · 8.8s TTFT (warm) · 55 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 22.1 tok/s | 4778 ms | 8K |
| Coding | B | Tight fit | 22.1 tok/s | 8760 ms | 8K |
| Agentic Coding | C | Very compromised | 11.5 tok/s | 24445 ms | 8K |
| Reasoning | B | Tight fit | 22.1 tok/s | 10353 ms | 8K |
| RAG | C | Very compromised | 11.5 tok/s | 30556 ms | 8K |
Quantization options
How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B62 |
Q3_K_S | 3 | 14.7 GB | Low | B63 |
NVFP4 | 4 |
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 99Upgrade options
Hardware that runs MPT-30B-Instruct well
96 GB VRAM (+32)1597 GB/s (+997)
A
Raises estimated decode speed by about 186%.63.3 tok/s decode
Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
A
Raises estimated decode speed by about 592%.153 tok/s decode
Raises estimated decode speed by about 592%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP