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URL: https://willitrunai.com/can-run/mpt-30b-instruct-on-rtx-4070-12gb

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Can MPT-30B-Instruct run on RTX 4070 12GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

MPT-30B-Instruct needs ~47.4 GB but RTX 4070 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity
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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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 47.4 GB, exceeds 12.0 GB available
47.4 GB required12.0 GB available
395% VRAM needed

35.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.7 tok/s

TTFT

72306 ms

Safe context

4K

Memory

47.4 GB / 12.0 GB

Offload

70%

Memory breakdown

Weights21.6 GB
KV Cache23.4 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMPT-30B-Instruct on RTX 4070 12GB
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: 2.7 tok/s decode · 72.3s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 47.4 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.7 tok/s39440 ms4K
CodingFToo heavy2.7 tok/s72306 ms4K
Agentic CodingFToo heavy2.7 tok/s105173 ms4K
ReasoningFToo heavy2.7 tok/s85453 ms4K
RAGFToo heavy2.7 tok/s131466 ms4K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.7 GB
LowF0
Q3_K_S
3
14.7 GB
LowF0
NVFP4
4

Upgrade options

Hardware that runs MPT-30B-Instruct well

👁 NVIDIA
RTX A6000 48GBBudget pick
48 GB VRAM (+36)768 GB/s (+264)
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.

Raises estimated decode speed by about 574%.

~$4,650 MSRP

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBBest value
48 GB VRAM (+36)1344 GB/s (+840)
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 1233%.

~$4,999 MSRP

👁 NVIDIA
Quadro RTX 8000 48GBNVIDIA upgrade
48 GB VRAM (+36)672 GB/s (+168)
B
Makes the model fit on the accelerator instead of staying completely out of reach.14 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 419%.

~$5,800 MSRP

👁 NVIDIA
NVIDIA H100 80GBBiggest leap
80 GB VRAM (+68)3350 GB/s (+2846)
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

Frequently asked questions

See all results for RTX 4070 12GBSee all hardware for MPT-30B-Instruct
16.8 GB
Medium
F0
Q4_K_M
4
18.3 GB
MediumF0
Q5_K_M
5
21.6 GB
HighF0
Q6_K
6
24.6 GB
HighF0
Q8_0
8
32.1 GB
Very HighF0
F16
16
61.5 GB
MaximumF0

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.