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Can MPT-7B-Instruct run on RTX 3090 24GB?

YES — Runs Great

A72Great
Estimated from fit model

MPT-7B-Instruct needs ~15.7 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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

Q4_K_M (Medium quality) — 15.7 GB, 98.0 tok/s, Runs well
15.7 GB required24.0 GB available
65% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

8K

Memory

15.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsMPT-7B-Instruct on RTX 3090 24GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well98.0 tok/s1078 ms8K
CodingARuns well98.0 tok/s1976 ms8K
Agentic CodingARuns with offload98.0 tok/s2873 ms8K
ReasoningARuns well98.0 tok/s2335 ms8K
RAGARuns with offload98.0 tok/s3592 ms8K

Inference speed

MPT-7B-Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for MPT-7B-Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M98.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M98.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M98.0Tight
NVIDIARTX 3090 24GB
24 GBQ4_K_M98.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M98.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M98.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M98.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M98.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M87.8Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M87.8Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M56.2Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M51.5Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M45.3Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M43.0Heavy offload
NVIDIARTX 3060 12GB
12 GBQ4_K_M24.7Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M10.6Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How MPT-7B-Instruct (7B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB60
Q3_K_S
3
3.4 GB
LowB61
NVFP4
4
3.9 GB
MediumB61
Q4_K_M
4
4.3 GB
MediumB61
Q5_K_M
5
5.0 GB
HighB62
Q6_K
6
5.7 GB
HighB62
Q8_0
8
7.5 GB
Very HighB63
F16Best for your GPU
16
14.3 GB
MaximumB66

Get started

Copy-paste commands to run MPT-7B-Instruct on your machine.

Run

lms load mpt-7b-instruct && lms server start

Your hardware

More models your RTX 3090 24GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS99.1 tok/s
AlibabaQwen 3.5 27B27BS43 tok/s
AlibabaQwen 3.6 27B27BS43.1 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS102.5 tok/s
AlibabaQwen 3.5 9B9BS126 tok/s

Frequently asked questions

Can RTX 3090 24GB run MPT-7B-Instruct?

Yes, RTX 3090 24GB can run MPT-7B-Instruct with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does MPT-7B-Instruct need?

MPT-7B-Instruct (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for MPT-7B-Instruct?

The recommended quantization for MPT-7B-Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will MPT-7B-Instruct run at on RTX 3090 24GB?

On RTX 3090 24GB, MPT-7B-Instruct achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 3090 24GB run MPT-7B-Instruct for coding?

For coding workloads, MPT-7B-Instruct on RTX 3090 24GB receives a A grade with 98.0 tok/s and 8K context.

What context window can MPT-7B-Instruct use on RTX 3090 24GB?

On RTX 3090 24GB, MPT-7B-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.

See all results for RTX 3090 24GBSee all hardware for MPT-7B-Instruct
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