Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+76)
C
This setup is broadly balanced for this model.22.4 tok/s Dekodierung
ca. $6,999 MSRP
Can logos16v2 stablelm2 1.6b i1 run on NVIDIA B200 180GB?
YES — Runs Great
D39Poor
Estimated from fit model
logos16v2 stablelm2 1.6b i1 needs ~20.4 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 20.4 GB, 22.4 tok/s, Runs well
20.4 GB required180.0 GB available
11% VRAM used
Fit status
Runs well
Decode
22.4 tok/s
TTFT
8643 ms
Safe context
13.6M
Memory
20.4 GB / 180.0 GB
Memory breakdown
Weights1.0 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelslogos16v2 stablelm2 1.6b i1 on NVIDIA B200 180GB
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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 22.4 tok/s | 4714 ms | 12.8M |
| Coding | D | Runs well | 22.4 tok/s | 8643 ms | 13.6M |
| Agentic Coding | D | Runs well | 22.4 tok/s | 12571 ms | 13.6M |
| Reasoning | D | Runs well | 22.4 tok/s | 10214 ms | 13.6M |
| RAG | D | Runs well | 22.4 tok/s | 15714 ms | 13.6M |
Quantization options
How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D37 |
Q3_K_S | 3 | 0.8 GB | Low | D37 |
NVFP4 | 4 | 0.9 GB | Medium | D37 |
Q4_K_M | 4 | 1.0 GB | Medium | D37 |
Q5_K_M | 5 | 1.2 GB | High | D37 |
Q6_K | 6 | 1.3 GB | High | D37 |
Q8_0 | 8 | 1.7 GB | Very High | D37 |
F16Best for your GPU | 16 | 3.3 GB | Maximum | D37 |
Get started
Copy-paste commands to run logos16v2 stablelm2 1.6b i1 on your machine.
Run
lms load hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf && lms server startUpgrade-Optionen
Hardware, die logos16v2 stablelm2 1.6b i1 gut ausführt
Frequently asked questions
Can NVIDIA B200 180GB run logos16v2 stablelm2 1.6b i1?
Yes, NVIDIA B200 180GB can run logos16v2 stablelm2 1.6b i1 with a D grade (Runs well). Expected decode speed: 22.4 tok/s.
How much VRAM does logos16v2 stablelm2 1.6b i1 need?
logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.
What is the best quantization for logos16v2 stablelm2 1.6b i1?
The recommended quantization for logos16v2 stablelm2 1.6b i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will logos16v2 stablelm2 1.6b i1 run at on NVIDIA B200 180GB?
On NVIDIA B200 180GB, logos16v2 stablelm2 1.6b i1 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.
Can NVIDIA B200 180GB run logos16v2 stablelm2 1.6b i1 for coding?
For coding workloads, logos16v2 stablelm2 1.6b i1 on NVIDIA B200 180GB receives a D grade with 22.4 tok/s and 13.6M context.
What context window can logos16v2 stablelm2 1.6b i1 use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, logos16v2 stablelm2 1.6b i1 can safely use up to 13.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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