Will It Run AI

Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on NVIDIA GH200 96GB?

YES — Runs Great

C48Usable
Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~28.3 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~221 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) — 28.3 GB, 221.3 tok/s, Runs well
28.3 GB required96.0 GB available
29% VRAM used

Fit status

Runs well

Decode

221.3 tok/s

TTFT

875 ms

Safe context

401K

Memory

28.3 GB / 96.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA GH200 96GB
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: 221.3 tok/s decode · 875ms TTFT (warm) · 553 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
ChatCRuns well221.3 tok/s477 ms401K
CodingCRuns well221.3 tok/s875 ms401K
Agentic CodingCRuns well221.3 tok/s1272 ms401K
ReasoningCRuns well221.3 tok/s1034 ms401K
RAGCRuns well221.3 tok/s1591 ms401K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD39
Q3_K_S
3
11.8 GB
LowD40
NVFP4
4
13.4 GB
MediumD40
Q4_K_M
4
14.6 GB
MediumD40
Q5_K_M
5
17.3 GB
HighC40
Q6_K
6
19.7 GB
HighC41
Q8_0
8
25.7 GB
Very HighC41
F16Best for your GPU
16
49.2 GB
MaximumC46

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 GH200 96GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, NVIDIA GH200 96GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 221.3 tok/s.

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

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 28.3 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 GH200 96GB?

On NVIDIA GH200 96GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 221.3 tokens per second decode speed with a time-to-first-token of 875ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA GH200 96GB receives a C grade with 221.3 tok/s and 401K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 401K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA GH200 96GBSee all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B
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