MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+32)
C
Raises estimated decode speed by about 43%.21.5 tok/s decode
Raises estimated decode speed by about 43%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on MacBook Pro M1 Max 32GB?
YES — Tight Fit
C48Usable
○Estimated from fit model
cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~21.8 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 21.8 GB, 15.0 tok/s, Tight fit
21.8 GB required23.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
15.0 tok/s
TTFT
12883 ms
Safe context
23K
Memory
21.8 GB / 23.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M1 Max 32GB
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: 15.0 tok/s decode · 12.9s TTFT (warm) · 38 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 15.0 tok/s | 7027 ms | 23K |
| Coding | C | Tight fit | 15.0 tok/s | 12883 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 13.4 tok/s | 21084 ms | 23K |
| Reasoning | C | Tight fit | 15.0 tok/s | 15226 ms | 23K |
| RAG | D | Runs with offload (needs ~0.9 GB host RAM) | 13.4 tok/s | 26355 ms | 23K |
Quantization options
How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | C50 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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 startUpgrade options
Hardware that runs cognitivecomputations Dolphin Mistral 24B Venice Edition well
MacBook Pro M4 Max 48GBBest value
48 GB Unified (+16)546 GB/s (+146)
C
Raises estimated decode speed by about 128%.34.2 tok/s decode
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
MacBook Pro M3 Max 48GBApple upgrade
48 GB Unified (+16)
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