NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+4527)
A
Raises estimated decode speed by about 716%.182 tok/s 解码
Raises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can OLMo 2 13B run on NVIDIA DGX Spark 128GB?
YES — Runs Great
B69Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 24.3 GB, 22.3 tok/s, Runs well
24.3 GB required108.8 GB available
22% VRAM used
Fit status
Runs well
Decode
22.3 tok/s
TTFT
8678 ms
Safe context
33K
Memory
24.3 GB / 108.8 GB
Memory breakdown
Weights7.9 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsOLMo 2 13B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 22.3 tok/s decode · 8.7s TTFT (warm) · 56 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| 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 |
Quantization options
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 | 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 |
Get started
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 99升级选项
能流畅运行 OLMo 2 13B 的硬件
NVIDIA H200 141GB经济之选NVIDIA H200 PCIe 141GB最佳性价比141 GB VRAM (+13)4800 GB/s (+4527)
A
Raises estimated decode speed by about 716%.182 tok/s 解码
Raises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA 升级180 GB VRAM (+52)8000 GB/s (+7727)
A
Raises estimated decode speed by about 716%.182 tok/s 解码
Raises estimated decode speed by about 716%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run OLMo 2 13B?
Yes, NVIDIA DGX Spark 128GB can run OLMo 2 13B with a B grade (Runs well). Expected decode speed: 22.3 tok/s.
How much VRAM does OLMo 2 13B need?
OLMo 2 13B (13B parameters) requires approximately 24.3 GB of memory with Q4_K_M quantization.
What is the best quantization for OLMo 2 13B?
The recommended quantization for OLMo 2 13B is Q4_K_M, which balances quality and memory efficiency.
What speed will OLMo 2 13B run at on NVIDIA DGX Spark 128GB?
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.
Can NVIDIA DGX Spark 128GB run OLMo 2 13B for coding?
For coding workloads, OLMo 2 13B on NVIDIA DGX Spark 128GB receives a B grade with 22.3 tok/s and 33K context.
What context window can OLMo 2 13B use on NVIDIA DGX Spark 128GB?
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.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for OLMo 2 13B?
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.
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