Can stabilityai japanese stablelm instruct beta 70b run on NVIDIA H20 96GB?

YES — Runs Great

C55Usable

Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~61.7 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~76 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) — 61.7 GB, 75.9 tok/s, Runs well

61.7 GB required96.0 GB available

64% VRAM used

Fit status

Runs well

Decode

75.9 tok/s

TTFT

2551 ms

Safe context

83K

Memory

61.7 GB / 96.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm instruct beta 70b on NVIDIA H20 96GB
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: 75.9 tok/s decode · 2.6s TTFT (warm) · 190 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	75.9 tok/s	1392 ms	83K
Coding	C	Runs well	75.9 tok/s	2551 ms	83K
Agentic Coding	B	Runs well	75.9 tok/s	3711 ms	83K
Reasoning	C	Runs well	75.9 tok/s	3015 ms	83K
RAG	B	Runs well	75.9 tok/s	4639 ms	83K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	C42
Q3_K_S	3	34.3 GB	Low	C43
NVFP4	4	39.2 GB	Medium	C44
Q4_K_M	4	42.7 GB	Medium	C45
Q5_K_M	5	50.4 GB	High	C47
Q6_K	6	57.4 GB	High	C47
Q8_0Best for your GPU	8	74.9 GB	Very High	C47
F16	16	143.5 GB	Maximum	F0

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 NVIDIA H20 96GB run stabilityai japanese stablelm instruct beta 70b?

Yes, NVIDIA H20 96GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 75.9 tok/s.

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

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 61.7 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 NVIDIA H20 96GB?

On NVIDIA H20 96GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 75.9 tokens per second decode speed with a time-to-first-token of 2551ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA H20 96GB receives a C grade with 75.9 tok/s and 83K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 83K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for stabilityai japanese stablelm instruct beta 70b

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