Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on NVIDIA H100 80GB?

YES — Runs Great

C49Usable

Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~26.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~192 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) — 26.7 GB, 192.2 tok/s, Runs well

26.7 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

192.2 tok/s

TTFT

1007 ms

Safe context

319K

Memory

26.7 GB / 80.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA H100 80GB
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: 192.2 tok/s decode · 1.0s TTFT (warm) · 481 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	C	Runs well	192.2 tok/s	549 ms	319K
Coding	C	Runs well	192.2 tok/s	1007 ms	319K
Agentic Coding	C	Runs well	192.2 tok/s	1465 ms	319K
Reasoning	C	Runs well	192.2 tok/s	1190 ms	319K
RAG	C	Runs well	192.2 tok/s	1831 ms	319K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C40
Q3_K_S	3	11.8 GB	Low	C40
NVFP4	4

Get started

Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.

Run

lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server start

Frequently asked questions

Can NVIDIA H100 80GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, NVIDIA H100 80GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 192.2 tok/s.

How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 26.7 GB of memory with Q4_K_M quantization.

What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?

The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 192.2 tokens per second decode speed with a time-to-first-token of 1007ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA H100 80GB receives a C grade with 192.2 tok/s and 319K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on NVIDIA H100 80GB?

See all results for NVIDIA H100 80GB See all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B

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