Can stabilityai japanese stablelm instruct beta 70b run on H100 NVL 188GB?

YES — Runs Great

C50Usable

Estimated from fit model

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.

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 70.9 GB, 148.0 tok/s, Runs well

70.9 GB required188.0 GB available

38% VRAM used

Fit status

Runs well

Decode

148.0 tok/s

TTFT

1308 ms

Safe context

244K

Memory

70.9 GB / 188.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm instruct beta 70b on H100 NVL 188GB
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: 148.0 tok/s decode · 1.3s TTFT (warm) · 370 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	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

Quantization options

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	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

Get started

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 start

Frequently asked questions

Can H100 NVL 188GB run stabilityai japanese stablelm instruct beta 70b?

Yes, H100 NVL 188GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 148.0 tok/s.

How much VRAM does stabilityai japanese stablelm instruct beta 70b need?

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 70.9 GB of memory with Q4_K_M quantization.

What is the best quantization for stabilityai japanese stablelm instruct beta 70b?

The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.

What speed will stabilityai japanese stablelm instruct beta 70b run at on H100 NVL 188GB?

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.

Can H100 NVL 188GB run stabilityai japanese stablelm instruct beta 70b for coding?

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.

What context window can stabilityai japanese stablelm instruct beta 70b use on H100 NVL 188GB?

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.

See all results for H100 NVL 188GB See all hardware for stabilityai japanese stablelm instruct beta 70b

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