Can Mistral 7B Instruct v0.3 run on NVIDIA GB200 192GB?

YES — Runs Great

B57Good

Estimated from fit model

Mistral 7B Instruct v0.3 needs ~26.6 GB VRAM. NVIDIA GB200 192GB has 192.0 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) — 26.6 GB, 98.0 tok/s, Runs well

26.6 GB required192.0 GB available

14% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

26.6 GB / 192.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsMistral 7B Instruct v0.3 on NVIDIA GB200 192GB
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	B	Runs well	98.0 tok/s	2873 ms	8K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	8K
RAG	B	Runs well	98.0 tok/s	3592 ms	8K

Quantization options

How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.

Run

lms load Mistral-7B-Instruct-v0.3 && lms server start

Frequently asked questions

Can NVIDIA GB200 192GB run Mistral 7B Instruct v0.3?

Yes, NVIDIA GB200 192GB can run Mistral 7B Instruct v0.3 with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does Mistral 7B Instruct v0.3 need?

Mistral 7B Instruct v0.3 (7B parameters) requires approximately 26.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral 7B Instruct v0.3?

The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral 7B Instruct v0.3 run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Mistral 7B Instruct v0.3 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run Mistral 7B Instruct v0.3 for coding?

For coding workloads, Mistral 7B Instruct v0.3 on NVIDIA GB200 192GB receives a B grade with 98.0 tok/s and 8K context.

What context window can Mistral 7B Instruct v0.3 use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Mistral 7B Instruct v0.3 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 GB200 192GB See all hardware for Mistral 7B Instruct v0.3

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