Will It Run AI

Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on RTX A5000 24GB?

YES — Tight Fit

C51Usable
Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.1 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 21.1 GB, 36.7 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used

Fit status

Tight fit

Decode

36.7 tok/s

TTFT

5272 ms

Safe context

33K

Memory

21.1 GB / 24.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX A5000 24GB
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: 36.7 tok/s decode · 5.3s TTFT (warm) · 92 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 well36.7 tok/s2876 ms33K
CodingCTight fit36.7 tok/s5272 ms33K
Agentic CodingCRuns with offload36.7 tok/s7669 ms33K
ReasoningCTight fit36.7 tok/s6231 ms33K
RAGCRuns with offload36.7 tok/s9586 ms33K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC49
Q3_K_S
3
11.8 GB
LowC50
NVFP4
4
13.4 GB
MediumC50
Q4_K_M
4
14.6 GB
MediumC50
Q5_K_MBest for your GPU
5
17.3 GB
HighC50
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem cognitivecomputations Dolphin3.0 R1 Mistral 24B

NVIDIARTX 5090 32GBOpção econômica
32 GB VRAM (+8)1792 GB/s (+1024)
B
Raises estimated decode speed by about 123%.82 tok/s decodificação

Raises estimated decode speed by about 123%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMelhor custo-benefício
32 GB VRAM (+8)896 GB/s (+128)
C
Raises estimated decode speed by about 40%.51.4 tok/s decodificação

Raises estimated decode speed by about 40%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBUpgrade NVIDIA
32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can RTX A5000 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, RTX A5000 24GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Tight fit). Expected decode speed: 36.7 tok/s.

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

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 21.1 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 RTX A5000 24GB?

On RTX A5000 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 36.7 tokens per second decode speed with a time-to-first-token of 5272ms using Q4_K_M quantization.

Can RTX A5000 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX A5000 24GB receives a C grade with 36.7 tok/s and 33K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on RTX A5000 24GB?

On RTX A5000 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A5000 24GBSee all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B
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