OLMo 2 13B needs ~12.9 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~59 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
58.8 tok/s
TTFT
3291 ms
Safe context
33K
Memory
12.9 GB / 16.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 58.8 tok/s | 1795 ms | 33K |
| Coding | A | Runs well | 58.8 tok/s | 3291 ms | 33K |
| Agentic Coding | A | Runs with offload | 58.8 tok/s | 4788 ms | 33K |
| Reasoning | A | Runs well | 58.8 tok/s | 3890 ms | 33K |
| RAG | A | Runs with offload | 58.8 tok/s | 5985 ms | 33K |
How OLMo 2 13B (13B params) fits at each quantization level on Tesla P100 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 | 54.6 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | S | 51.8 tok/s | |
 GPT-OSS 20B | 21B | A | 48.1 tok/s | |
 Ministral 3 14B | 14B | S | 54.4 tok/s | |
| Codestral 2 25.08 | 22B | A | 16.7 tok/s |
Yes, Tesla P100 16GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 58.8 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 Tesla P100 16GB, OLMo 2 13B achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3291ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on Tesla P100 16GB receives a A grade with 58.8 tok/s and 33K context.
On Tesla P100 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-tesla-p100-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: