Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on NVIDIA V100 32GB?

YES — Runs Great

C54Usable

Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 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) — 21.9 GB, 41.2 tok/s, Runs well

21.9 GB required32.0 GB available

68% VRAM used

Fit status

Runs well

Decode

41.2 tok/s

TTFT

4700 ms

Safe context

74K

Memory

21.9 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA V100 32GB
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: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 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	41.2 tok/s	2564 ms	74K
Coding	C	Runs well	41.2 tok/s	4700 ms	74K
Agentic Coding	C	Runs well	41.2 tok/s	6837 ms	74K
Reasoning	C	Runs well	41.2 tok/s	5555 ms	74K
RAG	C	Runs well	41.2 tok/s	8546 ms	74K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	82.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	52.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	47.2	Tight
RTX 3090 24GB	24 GB	Q4_K_M	44.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	38.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	31.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	30.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	19.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	6.7	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too 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 NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C46
Q3_K_S	3	11.8 GB	Low	C47
NVFP4	4	13.4 GB	Medium	C48
Q4_K_M	4	14.6 GB	Medium	C48
Q5_K_M	5	17.3 GB	High	C49
Q6_K	6	19.7 GB	High	C49
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

Opciones de mejora

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

NVIDIA A100 40GBOpción económica

40 GB VRAM (+8)1555 GB/s (+655)

Sube la velocidad estimada de decodificación alrededor de un 117%.89.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 117%.

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

~$10,000 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, NVIDIA V100 32GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 41.2 tok/s.

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

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 21.9 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 V100 32GB?

On NVIDIA V100 32GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 41.2 tokens per second decode speed with a time-to-first-token of 4700ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA V100 32GB receives a C grade with 41.2 tok/s and 74K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 74K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See 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-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: