Dolphin 2.9 8B needs ~8.8 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~24 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.5 GB host RAM)
Decode
26.1 tok/s
TTFT
7419 ms
Safe context
9K
Memory
8.8 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 42.9 tok/s | 2464 ms | 9K |
| Coding | C | Very compromised | 24.3 tok/s | 7976 ms | 9K |
| Agentic Coding | F | Too heavy | 17.1 tok/s | 16433 ms | 9K |
| Reasoning | C | Very compromised (needs ~0.5 GB host RAM) | 26.1 tok/s | 8768 ms | 9K |
| RAG | F | Too heavy | 17.1 tok/s | 20541 ms | 9K |
How Dolphin 2.9 8B (8B params) fits at each quantization level on RTX 4070 Laptop 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
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 100%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 134%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 77%.
~$499 MSRP
Yes, RTX 4070 Laptop 8GB can run Dolphin 2.9 8B with a C grade (Very compromised). Expected decode speed: 24.3 tok/s.
Dolphin 2.9 8B (8B parameters) requires approximately 8.8 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 RTX 4070 Laptop 8GB, Dolphin 2.9 8B achieves approximately 24.3 tokens per second decode speed with a time-to-first-token of 7976ms using Q4_K_M quantization.
For coding workloads, Dolphin 2.9 8B on RTX 4070 Laptop 8GB receives a C grade with 24.3 tok/s and 9K context.
On RTX 4070 Laptop 8GB, Dolphin 2.9 8B can safely use up to 9K 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-rtx-4070-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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.