OLMo 2 13B needs ~24.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~22 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
22.3 tok/s
TTFT
8678 ms
Safe context
33K
Memory
24.3 GB / 108.8 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 22.3 tok/s | 4734 ms | 33K |
| Coding | B | Runs well | 22.3 tok/s | 8678 ms | 33K |
| Agentic Coding | B | Runs well | 22.3 tok/s | 12623 ms | 33K |
| Reasoning | B | Runs well | 22.3 tok/s | 10256 ms | 33K |
| RAG | B | Runs well | 22.3 tok/s | 15779 ms | 33K |
How OLMo 2 13B (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B66 |
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 99Upgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run OLMo 2 13B with a B grade (Runs well). Expected decode speed: 22.3 tok/s.
OLMo 2 13B (13B parameters) requires approximately 24.3 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 NVIDIA DGX Spark 128GB, OLMo 2 13B achieves approximately 22.3 tokens per second decode speed with a time-to-first-token of 8678ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on NVIDIA DGX Spark 128GB receives a B grade with 22.3 tok/s and 33K context.
On NVIDIA DGX Spark 128GB, 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-dgx-spark-128gb" 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 |
| B66 |
Q4_K_M | 4 | 7.9 GB | Medium | B66 |
Q5_K_M | 5 | 9.4 GB | High | B66 |
Q6_K | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | B67 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B69 |
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.