Will It Run AI

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C48Usable
Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~28.7 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 28.7 GB, 34.2 tok/s, Runs well
28.7 GB required69.1 GB available
42% VRAM used

Fit status

Runs well

Decode

34.2 tok/s

TTFT

5663 ms

Safe context

246K

Memory

28.7 GB / 69.1 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M4 Max 96GB
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: 34.2 tok/s decode · 5.7s TTFT (warm) · 86 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 well34.2 tok/s3089 ms246K
CodingCRuns well34.2 tok/s5663 ms246K
Agentic CodingCRuns well34.2 tok/s8237 ms246K
ReasoningCRuns well34.2 tok/s6693 ms246K
RAGCRuns well34.2 tok/s10296 ms246K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC41
Q3_K_S
3
11.8 GB
LowC41
NVFP4
4
13.4 GB
MediumC42
Q4_K_M
4
14.6 GB
MediumC42
Q5_K_M
5
17.3 GB
HighC42
Q6_K
6
19.7 GB
HighC43
Q8_0
8
25.7 GB
Very HighC44
F16Best for your GPU
16
49.2 GB
MaximumC48

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

Upgrade-Optionen

Hardware, die cognitivecomputations Dolphin Mistral 24B Venice Edition gut ausführt

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl
1792 GB/s (+1246)
C
Raises estimated decode speed by about 201%.102.8 tok/s Dekodierung

Raises estimated decode speed by about 201%.

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

ca. $9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-Verhältnis
1597 GB/s (+1051)
C
Raises estimated decode speed by about 168%.91.6 tok/s Dekodierung

Raises estimated decode speed by about 168%.

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

ca. $9,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?

Yes, MacBook Pro M4 Max 96GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 34.2 tok/s.

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

cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 28.7 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 MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 34.2 tokens per second decode speed with a time-to-first-token of 5663ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?

For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M4 Max 96GB receives a C grade with 34.2 tok/s and 246K context.

What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 246K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for cognitivecomputations Dolphin Mistral 24B Venice Edition?

Not always. MacBook Pro M4 Max 96GB 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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