Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on NVIDIA A100 40GB?

YES — Runs Great

C54Usable

○Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~22.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~89 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) — 22.7 GB, 89.2 tok/s, Runs well

22.7 GB required40.0 GB available

57% VRAM used

Fit status

Runs well

Decode

89.2 tok/s

TTFT

2170 ms

Safe context

115K

Memory

22.7 GB / 40.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA A100 40GB
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: 89.2 tok/s decode · 2.2s TTFT (warm) · 223 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	89.2 tok/s	1184 ms	115K
Coding	C	Runs well	89.2 tok/s	2170 ms	115K
Agentic Coding	B	Runs well	89.2 tok/s	3156 ms	115K
Reasoning	C	Runs well	89.2 tok/s	2564 ms	115K
RAG	B	Runs well	89.2 tok/s	3945 ms	115K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C44
Q3_K_S	3	11.8 GB	Low	C45
NVFP4	4	13.4 GB	Medium	C45
Q4_K_M	4	14.6 GB	Medium	C46
Q5_K_M	5	17.3 GB	High	C47
Q6_K	6	19.7 GB	High	C48
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0

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

See all results for NVIDIA A100 40GB See all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B

URL: https://willitrunai.com/can-run/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-a100-40gb

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