OLMo 2 13B needs ~9.2 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q2_K quantization, expect ~25 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
4.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.5 tok/s
TTFT
18511 ms
Safe context
4K
Memory
12.1 GB / 8.0 GB
Offload
30%
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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 12.2 tok/s | 8629 ms | 4K |
| Coding | F | Too heavy | 10.5 tok/s | 18511 ms | 4K |
| Agentic Coding | F | Too heavy | 7.0 tok/s | 40348 ms | 4K |
| Reasoning | F | Too heavy | 10.5 tok/s | 21877 ms | 4K |
| RAG | F | Too heavy | 7.0 tok/s | 50435 ms | 4K |
How OLMo 2 13B (13B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.1 GB | Low | A80 |
Q3_K_S | 3 | 6.4 GB | Low | F0 |
Copy-paste commands to run OLMo 2 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "allenai/OLMo-2-13B-Instruct" \
--hf-file "OLMo-2-13B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 3060 12GBBudget pickMakes 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.
~$329 MSRP
RTX 5060 Ti 16GBBest valueMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes 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.
~$499 MSRP
Yes, RTX 2060 Super 8GB can run OLMo 2 13B at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.1 GB which exceeds available memory, but at Q2_K it needs only 9.2 GB. Expected decode speed: 25.2 tok/s.
OLMo 2 13B (13B parameters) requires approximately 12.1 GB at Q4_K_M quantization. On RTX 2060 Super 8GB, it fits at Q2_K using 9.2 GB.
The recommended quantization is Q4_K_M, but on RTX 2060 Super 8GB the best fitting quantization is Q2_K, which uses 9.2 GB.
On RTX 2060 Super 8GB, OLMo 2 13B achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7689ms using Q2_K quantization.
For coding workloads, OLMo 2 13B on RTX 2060 Super 8GB receives a F grade with 10.5 tok/s and 4K context.
On RTX 2060 Super 8GB, OLMo 2 13B can safely use up to 8K tokens of context at Q2_K quantization. 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/olmo-2-13b-on-rtx-2060-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:
| 4 |
7.3 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 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.