Will It Run AI

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on NVIDIA DGX Spark 128GB?

YES — With F16

C46Usable
Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~66.3 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
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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.

cognitivecomputations Dolphin Mistral 24B Venice Edition at Q4_K_M needs 18.7 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (66.3 GB) with maximum quality. 8 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.7 GB, 11.2 tok/s, Runs well
31.7 GB required108.8 GB available
29% VRAM used

Fit status

Runs well

Decode

11.2 tok/s

TTFT

17303 ms

Safe context

455K

Memory

31.7 GB / 108.8 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.2 tok/s decode · 17.3s TTFT (warm) · 28 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well11.2 tok/s9438 ms455K
CodingFToo heavy2.0 tok/s96130 ms4K
Agentic CodingCRuns well11.2 tok/s25169 ms455K
ReasoningCRuns well11.2 tok/s20450 ms455K
RAGCRuns well11.2 tok/s31461 ms455K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD40
Q3_K_S
3
11.8 GB
LowD40
NVFP4
4
13.4 GB
MediumC40
Q4_K_M
4
14.6 GB
MediumC40
Q5_K_M
5
17.3 GB
HighC41
Q6_K
6
19.7 GB
HighC41
Q8_0
8
25.7 GB
Very HighC42
F16Best for your GPU
16
49.2 GB
MaximumC47

Get started

Copy-paste commands to run cognitivecomputations Dolphin Mistral 24B Venice Edition on your machine.

Run

lms load hf-bartowski--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server start

アップグレードオプション

cognitivecomputations Dolphin Mistral 24B Venice Editionを快適に動かすハードウェア

NVIDIANVIDIA H200 141GBコスパ重視
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 2359%.275.4 tok/sデコード

Raises estimated decode speed by about 2359%.

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

〜$30,000 MSRP

NVIDIANVIDIA H200 PCIe 141GBコスパ最良
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 2359%.275.4 tok/sデコード

Raises estimated decode speed by about 2359%.

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

〜$30,000 MSRP

NVIDIANVIDIA B200 180GBNVIDIAアップグレード
180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 2900%.336 tok/sデコード

Raises estimated decode speed by about 2900%.

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

〜$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?

Yes, NVIDIA DGX Spark 128GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition at F16 quantization (Runs well). The recommended Q4_K_M requires 18.7 GB which exceeds available memory, but at F16 it needs only 66.3 GB. Expected decode speed: 4.7 tok/s.

How much VRAM does cognitivecomputations Dolphin Mistral 24B Venice Edition need?

cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 18.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 66.3 GB.

What is the best quantization for cognitivecomputations Dolphin Mistral 24B Venice Edition?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 66.3 GB.

What speed will cognitivecomputations Dolphin Mistral 24B Venice Edition run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41536ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?

For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 258K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if cognitivecomputations Dolphin Mistral 24B Venice Edition feels slow on NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for cognitivecomputations Dolphin Mistral 24B Venice Edition?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GBSee all hardware for cognitivecomputations Dolphin Mistral 24B Venice Edition
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