VOOZH about

URL: https://willitrunai.com/can-run/mpt-7b-instruct-on-rtx-3080-10gb

⇱ MPT-7B-Instruct on RTX 3080 10GB? No — Alternatives

Can MPT-7B-Instruct run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

MPT-7B-Instruct needs ~14.3 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

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

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.3 GB, exceeds 10.0 GB available
14.3 GB required10.0 GB available
143% VRAM needed

4.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

47.9 tok/s

TTFT

4041 ms

Safe context

7K

Memory

14.3 GB / 10.0 GB

Offload

30%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMPT-7B-Instruct on RTX 3080 10GB
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: 47.9 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.3 GB, but this setup only exposes 10.0 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
ChatARuns with offload (needs ~0.2 GB host RAM)93.9 tok/s1125 ms7K
CodingFToo heavy47.9 tok/s4041 ms7K
Agentic CodingFToo heavy20.3 tok/s13878 ms7K
ReasoningFToo heavy47.9 tok/s4776 ms7K
RAGFToo heavy20.3 tok/s17348 ms7K

Quantization options

How MPT-7B-Instruct (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB67
Q3_K_S
3
3.4 GB
LowB68
NVFP4
4
3.9 GB
MediumB69
Q4_K_M
4
4.3 GB
MediumB69
Q5_K_M
5
5.0 GB
HighB69
Q6_KBest for your GPU
6
5.7 GB
HighB69
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Upgrade options

Hardware that runs MPT-7B-Instruct well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.65 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.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.51.3 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.

~$625 MSRP

Frequently asked questions

See all results for RTX 3080 10GBSee all hardware for MPT-7B-Instruct