Can stabilityai japanese stablelm instruct beta 70b run on NVIDIA GB200 192GB?

YES — Runs Great

C49Usable

○Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~71.3 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~157 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) — 71.3 GB, 157.4 tok/s, Runs well

71.3 GB required192.0 GB available

37% VRAM used

Fit status

Runs well

Decode

157.4 tok/s

TTFT

1230 ms

Safe context

251K

Memory

71.3 GB / 192.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsstabilityai japanese stablelm instruct beta 70b on NVIDIA GB200 192GB
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: 157.4 tok/s decode · 1.2s TTFT (warm) · 393 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	157.4 tok/s	671 ms	251K
Coding	C	Runs well	157.4 tok/s	1230 ms	251K
Agentic Coding	C	Runs well	157.4 tok/s	1789 ms	251K
Reasoning	C	Runs well	157.4 tok/s	1454 ms	251K
RAG	C	Runs well	157.4 tok/s	2237 ms	251K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 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	D40
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	C43
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

See all results for NVIDIA GB200 192GB See all hardware for stabilityai japanese stablelm instruct beta 70b

URL: https://willitrunai.com/can-run/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-gb200-192gb

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