OLMo 2 13B needs ~13.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~35 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
38.2 tok/s
TTFT
5062 ms
Safe context
33K
Memory
13.3 GB / 20.0 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 | 35.4 tok/s | 2982 ms | 33K |
| Coding | A | Runs well | 35.4 tok/s | 5467 ms | 33K |
| Agentic Coding | A | Runs well | 35.4 tok/s | 7953 ms | 33K |
| Reasoning | A | Runs well | 35.4 tok/s | 6461 ms | 33K |
| RAG | A | Runs well | 35.4 tok/s | 9941 ms | 33K |
How OLMo 2 13B (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A74 |
Q3_K_S | 3 | 6.4 GB | Low | A75 |
NVFP4 | 4 |
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 |
|---|---|---|---|---|
| 30.5B | A | 23.8 tok/s | ||
| 27B | A | 10.7 tok/s |
Yes, RTX 4000 Ada 20GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 35.4 tok/s.
OLMo 2 13B (13B parameters) requires approximately 13.3 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 4000 Ada 20GB, OLMo 2 13B achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5467ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on RTX 4000 Ada 20GB receives a A grade with 35.4 tok/s and 33K context.
On RTX 4000 Ada 20GB, 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-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A75 |
Q4_K_M | 4 | 7.9 GB | Medium | A76 |
Q5_K_M | 5 | 9.4 GB | High | A77 |
Q6_K | 6 | 10.7 GB | High | A78 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A77 |
F16 | 16 | 26.7 GB | Maximum | F0 |
| 27B | S | 10.1 tok/s |
| 30B | A | 25.3 tok/s |
| 24B | S | 20.6 tok/s |