OLMo 2 13B needs ~13.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~28 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
27.8 tok/s
TTFT
6963 ms
Safe context
33K
Memory
13.7 GB / 24.0 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 | 27.8 tok/s | 3798 ms | 33K |
| Coding | A | Runs well | 27.8 tok/s | 6963 ms | 33K |
| Agentic Coding | A | Runs well | 27.8 tok/s | 10129 ms | 33K |
| Reasoning | A | Runs well | 27.8 tok/s | 8230 ms | 33K |
| RAG | A | Runs well | 27.8 tok/s | 12661 ms | 33K |
How OLMo 2 13B (13B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A72 |
Q3_K_S | 3 | 6.4 GB | Low | A73 |
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 | S | 30.9 tok/s | ||
| 27B | S | 13.4 tok/s |
Yes, Tesla P40 24GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 27.8 tok/s.
OLMo 2 13B (13B parameters) requires approximately 13.7 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 P40 24GB, OLMo 2 13B achieves approximately 27.8 tokens per second decode speed with a time-to-first-token of 6963ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on Tesla P40 24GB receives a A grade with 27.8 tok/s and 33K context.
On Tesla P40 24GB, 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-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A74 |
Q4_K_M | 4 | 7.9 GB | Medium | A74 |
Q5_K_M | 5 | 9.4 GB | High | A75 |
Q6_K | 6 | 10.7 GB | High | A76 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A77 |
F16 | 16 | 26.7 GB | Maximum | F0 |
| 27B | S | 10.2 tok/s |
| 35B | A | 12.7 tok/s |
| 30B | S | 31.9 tok/s |