stabilityai japanese stablelm instruct beta 70b needs ~70.9 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~148 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
148.0 tok/s
TTFT
1308 ms
Safe context
244K
Memory
70.9 GB / 188.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 148.0 tok/s | 714 ms | 244K |
| Coding | C | Runs well | 148.0 tok/s | 1308 ms | 244K |
| Agentic Coding | C | Runs well | 148.0 tok/s | 1903 ms | 244K |
| Reasoning | C | Runs well | 148.0 tok/s | 1546 ms | 244K |
| RAG | C | Runs well | 148.0 tok/s | 2379 ms | 244K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | D38 |
Q3_K_S | 3 | 34.3 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run stabilityai japanese stablelm instruct beta 70b on your machine.
Run
lms load hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf && lms server startYes, H100 NVL 188GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 148.0 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 70.9 GB of memory with Q4_K_M quantization.
The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.
On H100 NVL 188GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 148.0 tokens per second decode speed with a time-to-first-token of 1308ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on H100 NVL 188GB receives a C grade with 148.0 tok/s and 244K context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-h100-nvl-188gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
39.2 GB |
| Medium |
| D40 |
Q4_K_M | 4 | 42.7 GB | Medium | C40 |
Q5_K_M | 5 | 50.4 GB | High | C41 |
Q6_K | 6 | 57.4 GB | High | C42 |
Q8_0 | 8 | 74.9 GB | Very High | C44 |
F16Best for your GPU | 16 | 143.5 GB | Maximum | C47 |
On H100 NVL 188GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 244K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.