Will It Run AI

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on MacBook Pro M3 Max 128GB?

YES — Runs Great

C43Usable
Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~32.2 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 32.2 GB, 16.4 tok/s, Runs well
32.2 GB required92.2 GB available
35% VRAM used

Fit status

Runs well

Decode

16.4 tok/s

TTFT

11810 ms

Safe context

357K

Memory

32.2 GB / 92.2 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M3 Max 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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 well16.4 tok/s6442 ms357K
CodingCRuns well16.4 tok/s11810 ms357K
Agentic CodingCRuns well16.4 tok/s17178 ms357K
ReasoningCRuns well16.4 tok/s13957 ms357K
RAGCRuns well16.4 tok/s21472 ms357K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD39
Q3_K_S
3
11.8 GB
LowD39
NVFP4
4
13.4 GB
MediumD40
Q4_K_M
4
14.6 GB
MediumD40
Q5_K_M
5
17.3 GB
HighC40
Q6_K
6
19.7 GB
HighC40
Q8_0
8
25.7 GB
Very HighC41
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-yixman--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server start

升级选项

能流畅运行 cognitivecomputations Dolphin Mistral 24B Venice Edition 的硬件

Mac Studio M3 Ultra 256GB经济之选
256 GB Unified (+128)819 GB/s (+419)
C
Raises estimated decode speed by about 132%.38 tok/s 解码

Raises estimated decode speed by about 132%.

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

~$6,999 MSRP

AMD Instinct MI350X 288GB最佳性价比
288 GB VRAM (+160)8000 GB/s (+7600)
C
Raises estimated decode speed by about 1949%.336 tok/s 解码

Raises estimated decode speed by about 1949%.

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

~$8,000 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?

Yes, MacBook Pro M3 Max 128GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 16.4 tok/s.

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

cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 32.2 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 MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11810ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?

For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M3 Max 128GB receives a C grade with 16.4 tok/s and 357K context.

What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 357K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for cognitivecomputations Dolphin Mistral 24B Venice Edition?

Not always. MacBook Pro M3 Max 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.

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