MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)
C
Raises estimated decode speed by about 33%.34.2 tok/s 解码
Raises estimated decode speed by about 33%.
~$2,499 MSRP
Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C52Usable
Estimated from fit model
cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~21.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 21.6 GB, 25.8 tok/s, Runs well
21.6 GB required32.0 GB available
68% VRAM used
Fit status
Runs well
Decode
25.8 tok/s
TTFT
7506 ms
Safe context
75K
Memory
21.6 GB / 32.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin Mistral 24B Venice Edition on Radeon AI PRO R9700 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 25.8 tok/s | 4094 ms | 75K |
| Coding | C | Runs well | 25.8 tok/s | 7506 ms | 75K |
| Agentic Coding | C | Runs well | 25.8 tok/s | 10918 ms | 75K |
| Reasoning | C | Runs well | 25.8 tok/s | 8871 ms | 75K |
| RAG | C | Runs well | 25.8 tok/s | 13647 ms | 75K |
Quantization options
How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C46 |
Q3_K_S | 3 | 11.8 GB | Low | C47 |
NVFP4 | 4 | 13.4 GB | Medium | C48 |
Q4_K_M | 4 | 14.6 GB | Medium | C48 |
Q5_K_M | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C49 |
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 start升级选项
能流畅运行 cognitivecomputations Dolphin Mistral 24B Venice Edition 的硬件
Frequently asked questions
Can Radeon AI PRO R9700 32GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?
Yes, Radeon AI PRO R9700 32GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 25.8 tok/s.
How much VRAM does cognitivecomputations Dolphin Mistral 24B Venice Edition need?
cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 21.6 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 Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7506ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?
For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on Radeon AI PRO R9700 32GB receives a C grade with 25.8 tok/s and 75K context.
What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 75K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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