cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~18.8 GB VRAM. RX 9070 16GB has 16.0 GB. With NVFP4 quantization, expect ~17 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
4.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.2 tok/s
TTFT
14654 ms
Safe context
4K
Memory
20.0 GB / 16.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~2 GB host RAM) | 15.3 tok/s | 6886 ms | 4K |
| Coding | F | Too heavy | 13.2 tok/s | 14654 ms | 4K |
| Agentic Coding | F | Too heavy | 10.1 tok/s | 27895 ms | 4K |
| Reasoning | F | Too heavy | 13.2 tok/s | 17319 ms | 4K |
| RAG | F | Too heavy | 10.1 tok/s | 34869 ms | 4K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C51 |
Q3_K_SBest for your GPU | 3 | 11.8 GB | Low | C51 |
NVFP4 | 4 | 13.4 GB | Medium | F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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 startアップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,899 MSRP
Yes, RX 9070 16GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B at NVFP4 quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 20.0 GB which exceeds available memory, but at NVFP4 it needs only 18.8 GB. Expected decode speed: 17.1 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 20.0 GB at Q4_K_M quantization. On RX 9070 16GB, it fits at NVFP4 using 18.8 GB.
The recommended quantization is Q4_K_M, but on RX 9070 16GB the best fitting quantization is NVFP4, which uses 18.8 GB.
On RX 9070 16GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 17.1 tokens per second decode speed with a time-to-first-token of 11290ms using NVFP4 quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RX 9070 16GB receives a F grade with 13.2 tok/s and 4K context.
On RX 9070 16GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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-rx-9070-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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