willitrun·ai

Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on RTX 3090 24GB?

YES — Tight Fit

C52Usable
Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.1 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 44.8 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used

Fit status

Tight fit

Decode

44.8 tok/s

TTFT

4326 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 3090 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: 44.8 tok/s decode · 4.3s TTFT (warm) · 112 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 well44.8 tok/s2360 ms33K
CodingCTight fit44.8 tok/s4326 ms33K
Agentic CodingCRuns with offload44.8 tok/s6292 ms33K
ReasoningCTight fit44.8 tok/s5112 ms33K
RAGCRuns with offload44.8 tok/s7865 ms33K

Inference speed

cognitivecomputations Dolphin3.0 R1 Mistral 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for cognitivecomputations Dolphin3.0 R1 Mistral 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M82.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M52.3Tight
RX 7900 XTX 24GB
24 GBQ4_K_M47.2Tight
NVIDIARTX 3090 24GB
24 GBQ4_K_M44.8Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M38.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M34.2Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M34.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M31.7Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M30.1Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M19.1Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M16.4Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M15.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M6.7Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M4.2Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RTX 3090 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

Opciones de mejora

Hardware que ejecuta bien cognitivecomputations Dolphin3.0 R1 Mistral 24B

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+856)
B
Sube la velocidad estimada de decodificación alrededor de un 83%.82 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 83%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.51.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.31.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can RTX 3090 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, RTX 3090 24GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Tight fit). Expected decode speed: 44.8 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 3090 24GB?

On RTX 3090 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 44.8 tokens per second decode speed with a time-to-first-token of 4326ms using Q4_K_M quantization.

Can RTX 3090 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX 3090 24GB receives a C grade with 44.8 tok/s and 33K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on RTX 3090 24GB?

On RTX 3090 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 3090 24GBSee all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-rtx-3090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: