OLMo 2 7B needs ~7.9 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 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 with offload
Decode
64.4 tok/s
TTFT
3005 ms
Safe context
4K
Memory
7.9 GB / 8.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 64.4 tok/s | 1639 ms | 4K |
| Coding | A | Runs with offload | 64.4 tok/s | 3005 ms | 4K |
| Agentic Coding | F | Too heavy | 31.0 tok/s | 9081 ms | 4K |
| Reasoning | A | Runs with offload | 64.4 tok/s | 3551 ms | 4K |
| RAG | F | Too heavy | 31.0 tok/s | 11351 ms | 4K |
How OLMo 2 7B (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A74 |
NVFP4 | 4 | 3.9 GB | Medium | A74 |
Q4_K_M | 4 | 4.3 GB | Medium | A74 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run OLMo 2 7B on your machine.
Run
ollama run olmo2:7bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | A | 27.4 tok/s | |
| Qwen 3 8B | 8B | A | 29.9 tok/s | |
 Nemotron Nano 8B | 8B | A | 36.9 tok/s | |
 InternVL2 8B | 8B | A | 36.9 tok/s | |
 Ministral 3 8B | 8B | A | 34.8 tok/s |
Yes, RTX 3060 Ti 8GB can run OLMo 2 7B with a A grade (Runs with offload). Expected decode speed: 64.4 tok/s.
OLMo 2 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3060 Ti 8GB, OLMo 2 7B achieves approximately 64.4 tokens per second decode speed with a time-to-first-token of 3005ms using Q4_K_M quantization.
For coding workloads, OLMo 2 7B on RTX 3060 Ti 8GB receives a A grade with 64.4 tok/s and 4K context.
On RTX 3060 Ti 8GB, OLMo 2 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/olmo-2-7b-on-rtx-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: