OLMo 2 13B needs ~12.5 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 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
33.3 tok/s
TTFT
5810 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 | 48.2 tok/s | 2192 ms | 13K |
| Coding | A | Runs with offload | 29.4 tok/s | 6588 ms | 13K |
| Agentic Coding | F | Too heavy | 22.9 tok/s | 12312 ms | 13K |
| Reasoning | A | Runs with offload (needs ~0.3 GB host RAM) | 33.3 tok/s | 6866 ms | 13K |
| RAG | F | Too heavy | 22.9 tok/s | 15390 ms | 13K |
How OLMo 2 13B (13B params) fits at each quantization level on RTX 4080 Laptop 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 |
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 |
|---|---|---|---|---|
| 14B | A | 30.5 tok/s | ||
| 14.7B | A | 22.2 tok/s |
Yes, RTX 4080 Laptop 12GB can run OLMo 2 13B with a A grade (Runs with offload). Expected decode speed: 29.4 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 4080 Laptop 12GB, OLMo 2 13B achieves approximately 29.4 tokens per second decode speed with a time-to-first-token of 6588ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on RTX 4080 Laptop 12GB receives a A grade with 29.4 tok/s and 13K context.
On RTX 4080 Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/olmo-2-13b-on-rtx-4080-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
| 14B | A | 27.9 tok/s |
| 14B | A | 25.3 tok/s |
| 14B | A | 25.9 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.