MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
C
Raises estimated decode speed by about 74%.34.2 tok/s decode
Raises estimated decode speed by about 74%.
~$2,499 MSRP
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.6 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Operating mode
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
Fit status
Runs well
Decode
19.6 tok/s
TTFT
9885 ms
Safe context
75K
Memory
21.6 GB / 32.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 19.6 tok/s | 5392 ms | 75K |
| Coding | C | Runs well | 19.6 tok/s | 9885 ms | 75K |
| Agentic Coding | C | Runs well | 19.6 tok/s | 14379 ms | 75K |
| Reasoning | C | Runs well | 19.6 tok/s | 11683 ms | 75K |
| RAG | C | Runs well | 19.6 tok/s | 17973 ms | 75K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Radeon Pro W6800 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 |
Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.
Run
lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 74%.
~$2,499 MSRP
~$2,999 MSRP
Yes, Radeon Pro W6800 32GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 19.6 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 21.6 GB of memory with Q4_K_M quantization.
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W6800 32GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 19.6 tokens per second decode speed with a time-to-first-token of 9885ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Radeon Pro W6800 32GB receives a C grade with 19.6 tok/s and 75K context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-radeon-pro-w6800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 | C48 |
F16 | 16 | 49.2 GB | Maximum | F0 |
On Radeon Pro W6800 32GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 75K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.