MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
This setup is broadly balanced for this model.76.8 tok/s decode
~$2,499 MSRP
Dolphin3.0 Llama3.1 8B needs ~10.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~94 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
94.4 tok/s
TTFT
2050 ms
Safe context
388K
Memory
10.2 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 | 94.4 tok/s | 1118 ms | 388K |
| Coding | C | Runs well | 94.4 tok/s | 2050 ms | 388K |
| Agentic Coding | C | Runs well | 94.4 tok/s | 2982 ms | 388K |
| Reasoning | C | Runs well | 94.4 tok/s | 2423 ms | 388K |
| RAG | C | Runs well | 94.4 tok/s | 3728 ms | 388K |
How Dolphin3.0 Llama3.1 8B (8B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 | 4.5 GB | Medium | C43 |
Q4_K_M | 4 | 4.9 GB | Medium | C44 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
Copy-paste commands to run Dolphin3.0 Llama3.1 8B on your machine.
Run
lms load hf-bartowski--dolphin3-0-llama3-1-8b-gguf && lms server startUpgrade options
Yes, RTX 5000 Ada 32GB can run Dolphin3.0 Llama3.1 8B with a C grade (Runs well). Expected decode speed: 94.4 tok/s.
Dolphin3.0 Llama3.1 8B (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Dolphin3.0 Llama3.1 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5000 Ada 32GB, Dolphin3.0 Llama3.1 8B achieves approximately 94.4 tokens per second decode speed with a time-to-first-token of 2050ms using Q4_K_M quantization.
For coding workloads, Dolphin3.0 Llama3.1 8B on RTX 5000 Ada 32GB receives a C grade with 94.4 tok/s and 388K context.
On RTX 5000 Ada 32GB, Dolphin3.0 Llama3.1 8B can safely use up to 388K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--dolphin3-0-llama3-1-8b-gguf-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: