Will It Run AI

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on Gaudi 3 128GB?

YES — Runs Great

C47Usable
Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~31.2 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~177 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 31.2 GB, 176.9 tok/s, Runs well
31.2 GB required128.0 GB available
24% VRAM used

Fit status

Runs well

Decode

176.9 tok/s

TTFT

1094 ms

Safe context

567K

Memory

31.2 GB / 128.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on Gaudi 3 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: 176.9 tok/s decode · 1.1s TTFT (warm) · 442 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well176.9 tok/s597 ms567K
CodingCRuns well176.9 tok/s1094 ms567K
Agentic CodingCRuns well176.9 tok/s1592 ms567K
ReasoningCRuns well176.9 tok/s1293 ms567K
RAGCRuns well176.9 tok/s1990 ms567K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD38
Q3_K_S
3
11.8 GB
LowD38
NVFP4
4
13.4 GB
MediumD38
Q4_K_M
4
14.6 GB
MediumD38
Q5_K_M
5
17.3 GB
HighD38
Q6_K
6
19.7 GB
HighD39
Q8_0
8
25.7 GB
Very HighD39
F16Best for your GPU
16
49.2 GB
MaximumC43

Get started

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

Run

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

Frequently asked questions

Can Gaudi 3 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?

Yes, Gaudi 3 128GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 176.9 tok/s.

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

cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.

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

The recommended quantization for cognitivecomputations Dolphin Mistral 24B Venice Edition is Q4_K_M, which balances quality and memory efficiency.

What speed will cognitivecomputations Dolphin Mistral 24B Venice Edition run at on Gaudi 3 128GB?

On Gaudi 3 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 176.9 tokens per second decode speed with a time-to-first-token of 1094ms using Q4_K_M quantization.

Can Gaudi 3 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?

For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on Gaudi 3 128GB receives a C grade with 176.9 tok/s and 567K context.

What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on Gaudi 3 128GB?

On Gaudi 3 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 567K tokens of context. 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 Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for cognitivecomputations Dolphin Mistral 24B Venice Edition?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GBSee all hardware for cognitivecomputations Dolphin Mistral 24B Venice Edition
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