Qwen 3 8B needs ~8.8 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 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 ~0.4 GB host RAM)
Decode
35.8 tok/s
TTFT
5405 ms
Safe context
10K
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 60.2 tok/s | 1754 ms | 10K |
| Coding | A | Very compromised (needs ~0.4 GB host RAM) | 35.8 tok/s | 5405 ms | 10K |
| Agentic Coding | F | Too heavy | 21.9 tok/s | 12840 ms | 10K |
| Reasoning | A | Very compromised (needs ~0.4 GB host RAM) | 35.8 tok/s | 6387 ms | 10K |
| RAG | F | Too heavy | 21.9 tok/s | 16050 ms |
How Qwen 3 8B (8B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | S94 |
Q3_K_S | 3 | 3.9 GB | Low | S93 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 8B on your machine.
Run
ollama run qwen3:8bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | A | 27.5 tok/s |
Yes, RTX 2070 Super 8GB can run Qwen 3 8B with a A grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 35.8 tok/s.
Qwen 3 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, Qwen 3 8B achieves approximately 35.8 tokens per second decode speed with a time-to-first-token of 5405ms using Q4_K_M quantization.
For coding workloads, Qwen 3 8B on RTX 2070 Super 8GB receives a A grade with 35.8 tok/s and 10K context.
On RTX 2070 Super 8GB, Qwen 3 8B can safely use up to 10K tokens of context. The model's official context limit is 131K, 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/qwen-3-8b-on-rtx-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 10K |
| Medium |
| S93 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | S93 |
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.