OLMo 2 13B needs ~12.5 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~37 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
37.4 tok/s
TTFT
5179 ms
Safe context
13K
Memory
12.5 GB / 12.0 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 | 54.1 tok/s | 1954 ms | 13K |
| Coding | A | Runs with offload (needs ~0.3 GB host RAM) | 37.4 tok/s | 5179 ms | 13K |
| Agentic Coding | F | Too heavy | 25.7 tok/s | 10975 ms | 13K |
| Reasoning | A | Runs with offload (needs ~0.3 GB host RAM) | 37.4 tok/s | 6121 ms | 13K |
| RAG | F | Too heavy | 25.7 tok/s | 13719 ms | 13K |
How OLMo 2 13B (13B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A79 |
Q3_K_S | 3 | 6.4 GB | Low | A79 |
NVFP4 | 4 | 7.3 GB | Medium | A79 |
Q4_K_MBest for your GPU | 4 | 7.9 GB | Medium | A79 |
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 |
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 | A | 34.2 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | A | 24.9 tok/s | |
 Ministral 3 14B | 14B | A | 31.2 tok/s | |
| Phi-4 14B | 14B | A | 28.4 tok/s | |
| Qwen 2.5 14B | 14B | A | 29.1 tok/s |
Yes, RTX 4070 12GB can run OLMo 2 13B with a A grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 37.4 tok/s.
OLMo 2 13B (13B parameters) requires approximately 12.5 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 4070 12GB, OLMo 2 13B achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5179ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on RTX 4070 12GB receives a A grade with 37.4 tok/s and 13K context.
On RTX 4070 12GB, OLMo 2 13B can safely use up to 13K tokens of context. The model's official context limit is 33K, 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-2-13b-on-rtx-4070-12gb" 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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