stabilityai japanese stablelm instruct beta 70b needs ~60.1 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~66 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
65.9 tok/s
TTFT
2938 ms
Safe context
55K
Memory
60.1 GB / 80.0 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 | B | Runs well | 65.9 tok/s | 1602 ms | 55K |
| Coding | B | Runs well | 65.9 tok/s | 2938 ms | 55K |
| Agentic Coding | C | Tight fit | 65.9 tok/s | 4273 ms | 55K |
| Reasoning | B | Runs well | 65.9 tok/s | 3472 ms | 55K |
| RAG | C | Tight fit | 65.9 tok/s | 5341 ms | 55K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C43 |
Q3_K_S | 3 | 34.3 GB | Low | C45 |
NVFP4 | 4 | 39.2 GB | Medium | C46 |
Q4_K_M | 4 | 42.7 GB | Medium | C47 |
Q5_K_M | 5 | 50.4 GB | High | C47 |
Q6_KBest for your GPU | 6 | 57.4 GB | High | C47 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
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, NVIDIA H100 80GB can run stabilityai japanese stablelm instruct beta 70b with a B grade (Runs well). Expected decode speed: 65.9 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 60.1 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 NVIDIA H100 80GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 65.9 tokens per second decode speed with a time-to-first-token of 2938ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA H100 80GB receives a B grade with 65.9 tok/s and 55K context.
On NVIDIA H100 80GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 55K tokens of context. The model's official context limit is —, 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/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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