Can MPT-7B-Instruct run on NVIDIA V100 32GB?

YES — Runs Great

B69Good

Estimated from fit model

MPT-7B-Instruct needs ~16.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 16.5 GB, 98.0 tok/s, Runs well

16.5 GB required32.0 GB available

52% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

16.5 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsMPT-7B-Instruct on NVIDIA V100 32GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	98.0 tok/s	1078 ms	8K
Coding	B	Runs well	98.0 tok/s	1976 ms	8K
Agentic Coding	A	Runs well	98.0 tok/s	2873 ms	8K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	8K
RAG	A	Runs well	98.0 tok/s	3592 ms	8K

Quantization options

How MPT-7B-Instruct (7B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B59
Q3_K_S	3	3.4 GB	Low	B59
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs MPT-7B-Instruct well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.87.8 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run MPT-7B-Instruct?

Yes, NVIDIA V100 32GB can run MPT-7B-Instruct with a B 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 16.5 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, 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 NVIDIA V100 32GB run MPT-7B-Instruct for coding?

For coding workloads, MPT-7B-Instruct on NVIDIA V100 32GB receives a B grade with 98.0 tok/s and 8K context.

What context window can MPT-7B-Instruct use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, 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 NVIDIA V100 32GB See all hardware for MPT-7B-Instruct

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