OLMo 2 13B needs ~12.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 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 well
Decode
30.1 tok/s
TTFT
6439 ms
Safe context
33K
Memory
12.9 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 30.1 tok/s | 3512 ms | 33K |
| Coding | A | Runs well | 30.1 tok/s | 6439 ms | 33K |
| Agentic Coding | A | Runs with offload | 30.1 tok/s | 9367 ms | 33K |
| Reasoning | A | Runs well | 30.1 tok/s | 7610 ms | 33K |
| RAG | A | Runs with offload | 30.1 tok/s | 11708 ms | 33K |
How OLMo 2 13B (13B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A76 |
Q3_K_S | 3 | 6.4 GB | Low | A77 |
NVFP4 | 4 | 7.3 GB | Medium | A78 |
Q4_K_M | 4 | 7.9 GB | Medium | A79 |
Q5_K_M | 5 | 9.4 GB | High | A78 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A78 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | 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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3 14B | 14B | S | 30.4 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | S | 26 tok/s | |
 GPT-OSS 20B | 21B | A | 21.9 tok/s | |
 Ministral 3 14B | 14B | S | 27.8 tok/s | |
| Codestral 2 25.08 | 22B | B | 6.4 tok/s |
Yes, RTX 4060 Ti 16GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 30.1 tok/s.
OLMo 2 13B (13B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 13B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Ti 16GB, OLMo 2 13B achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6439ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on RTX 4060 Ti 16GB receives a A grade with 30.1 tok/s and 33K context.
On RTX 4060 Ti 16GB, OLMo 2 13B can safely use up to 33K 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/olmo-2-13b-on-rtx-4060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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