Will It Run AI

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C50Usable
Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~25.3 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~32 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) — 25.3 GB, 31.7 tok/s, Runs well
25.3 GB required46.1 GB available
55% VRAM used

Fit status

Runs well

Decode

31.7 tok/s

TTFT

6108 ms

Safe context

134K

Memory

25.3 GB / 46.1 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on Mac Studio M2 Ultra 64GB
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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well31.7 tok/s3332 ms134K
CodingCRuns well31.7 tok/s6108 ms134K
Agentic CodingCRuns well31.7 tok/s8885 ms134K
ReasoningCRuns well31.7 tok/s7219 ms134K
RAGCRuns well31.7 tok/s11106 ms134K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC43
Q3_K_S
3
11.8 GB
LowC43
NVFP4
4
13.4 GB
MediumC44
Q4_K_M
4
14.6 GB
MediumC44
Q5_K_M
5
17.3 GB
HighC45
Q6_K
6
19.7 GB
HighC46
Q8_0Best for your GPU
8
25.7 GB
Very HighC48
F16
16
49.2 GB
MaximumF0

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

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

NVIDIARTX PRO 5000 Blackwell 48GBコスパ重視
1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.77.1 tok/sデコード

Raises estimated decode speed by about 143%.

〜$4,999 MSRP

NVIDIARTX 6000 Ada 48GBコスパ最良
960 GB/s (+160)
C
Raises estimated decode speed by about 70%.53.8 tok/sデコード

Raises estimated decode speed by about 70%.

〜$6,800 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?

Yes, Mac Studio M2 Ultra 64GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 31.7 tok/s.

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

cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 25.3 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 Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6108ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?

For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on Mac Studio M2 Ultra 64GB receives a C grade with 31.7 tok/s and 134K context.

What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for cognitivecomputations Dolphin Mistral 24B Venice Edition?

Not always. Mac Studio M2 Ultra 64GB 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.

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