48 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.18.2 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$4,650 MSRP
Can MPT-30B-Instruct run on NVIDIA A100 40GB?
YES — With Q4_K_M
B62Good
○Estimated from fit model
MPT-30B-Instruct needs ~46.9 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload
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.
MPT-30B-Instruct at Q5_K_M needs 50.2 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q4_K_M (46.9 GB) with medium quality. 4 quantization levels fit.
Select quantization to explore
Q5_K_M (High quality) — 50.2 GB, exceeds 40.0 GB available
50.2 GB required40.0 GB available
126% VRAM needed
10.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
28.6 tok/s
TTFT
6761 ms
Safe context
8K
Memory
50.2 GB / 40.0 GB
Offload
20%
Memory breakdown
Weights21.6 GB
KV Cache23.4 GB
Runtime1.2 GB
Headroom4.0 GB
See how fast it feels
With memory offload — actual speed may be lowerSee how fast it feelsMPT-30B-Instruct on NVIDIA A100 40GB
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: 28.6 tok/s decode · 6.8s TTFT (warm) · 72 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 61.7 tok/s | 1712 ms | 8K |
| Coding | F | Too heavy | 28.6 tok/s | 6761 ms | 8K |
| Agentic Coding | F | Too heavy | 12.8 tok/s | 22019 ms | 8K |
| Reasoning | F | Too heavy | 28.6 tok/s | 7990 ms | 8K |
| RAG | F | Too heavy | 12.8 tok/s | 27523 ms | 8K |
Quantization options
How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B66 |
Q3_K_S | 3 | 14.7 GB | Low | B67 |
NVFP4 | 4 | 16.8 GB | Medium | B68 |
Q4_K_M | 4 | 18.3 GB | Medium | B68 |
Q5_K_M | 5 | 21.6 GB | High | B69 |
Q6_K | 6 | 24.6 GB | High | B69 |
Q8_0Best for your GPU | 8 | 32.1 GB | Very High | B69 |
F16 | 16 | 61.5 GB | Maximum | F0 |
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
48 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.36 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 26%.
~$4,999 MSRP
48 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.21 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$5,500 MSRP
80 GB VRAM (+40)3350 GB/s (+1795)
A
Makes the model fit on the accelerator instead of staying completely out of reach.132.9 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP