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URL: https://willitrunai.com/can-run/hf-bartowski--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf-on-m4-max-36gb

⇱ cognitivecomputations Dolphin Mistral 24B Venice Edition on…

Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on MacBook Pro M4 Max 36GB?

YES — Tight Fit

C50Usable
○Estimated from fit model

cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~22.2 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 22.2 GB, 27.3 tok/s, Tight fit
22.2 GB required25.9 GB available
86% VRAM used

Fit status

Tight fit

Decode

27.3 tok/s

TTFT

7102 ms

Safe context

37K

Memory

22.2 GB / 25.9 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom3.9 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M4 Max 36GB
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: 27.3 tok/s decode · 7.1s TTFT (warm) · 68 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 well27.3 tok/s3874 ms37K
CodingCTight fit27.3 tok/s7102 ms37K
Agentic CodingCRuns with offload27.3 tok/s10330 ms37K
ReasoningCTight fit27.3 tok/s8394 ms37K
RAGCRuns with offload27.3 tok/s12913 ms37K

Quantization options

How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC48
Q3_K_S
3
11.8 GB
LowC49
NVFP4
4
13.4 GB
MediumC50
Q4_K_M
4
14.6 GB
MediumC50
Q5_K_M
5
17.3 GB
HighC50
Q6_KBest for your GPU
6
19.7 GB
HighC49
Q8_0
8
25.7 GB
Very HighF0
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-bartowski--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server start

Upgrade options

Hardware that runs cognitivecomputations Dolphin Mistral 24B Venice Edition well

MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+12)546 GB/s (+136)
C
Raises estimated decode speed by about 25%.34.2 tok/s decode

Raises estimated decode speed by about 25%.

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

~$2,499 MSRP

MacBook Pro M3 Max 48GBBest value
48 GB Unified (+12)
C
Adds memory headroom for longer context windows and future model growth.16.4 tok/s decode

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

~$2,499 MSRP

MacBook Pro M4 Pro 48GBApple upgrade
48 GB Unified (+12)
C
Adds memory headroom for longer context windows and future model growth.21.5 tok/s decode

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

~$2,999 MSRP

Frequently asked questions

See all results for MacBook Pro M4 Max 36GBSee all hardware for cognitivecomputations Dolphin Mistral 24B Venice Edition