Dolphin 2.9 8B needs ~8.5 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
26.2 tok/s
TTFT
7398 ms
Safe context
12K
Memory
8.5 GB / 8.0 GB
Offload
10%
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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 40.0 tok/s | 2642 ms | 12K |
| Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 7398 ms | 12K |
| Agentic Coding | F | Too heavy | 17.0 tok/s | 16605 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 8743 ms | 12K |
| RAG | F | Too heavy | 17.0 tok/s | 20757 ms |
How Dolphin 2.9 8B (8B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run Dolphin 2.9 8B on your machine.
Run
ollama run dolphin-llama3Upgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 40%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 69%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 118%.
~$449 MSRP
Yes, RX 9060 8GB can run Dolphin 2.9 8B with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 26.2 tok/s.
Dolphin 2.9 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.
On RX 9060 8GB, Dolphin 2.9 8B achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7398ms using Q4_K_M quantization.
For coding workloads, Dolphin 2.9 8B on RX 9060 8GB receives a C grade with 26.2 tok/s and 12K context.
On RX 9060 8GB, Dolphin 2.9 8B can safely use up to 12K tokens of context. The model's official context limit is 33K, 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/dolphin-2.9-8b-on-rx-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 12K |
| Medium |
| C54 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C54 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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.