Will It Run AI

Can stabilityai japanese stablelm instruct beta 70b run on NVIDIA B200 180GB?

YES — Runs Great

C50Usable
Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~70.1 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~157 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 70.1 GB, 157.4 tok/s, Runs well
70.1 GB required180.0 GB available
39% VRAM used

Fit status

Runs well

Decode

157.4 tok/s

TTFT

1230 ms

Safe context

230K

Memory

70.1 GB / 180.0 GB

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime1.2 GB
Headroom18.0 GB

See how fast it feels

See how fast it feelsstabilityai japanese stablelm instruct beta 70b on NVIDIA B200 180GB
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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well157.4 tok/s671 ms230K
CodingCRuns well157.4 tok/s1230 ms230K
Agentic CodingCRuns well157.4 tok/s1789 ms230K
ReasoningCRuns well157.4 tok/s1454 ms230K
RAGCRuns well157.4 tok/s2237 ms230K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowD38
Q3_K_S
3
34.3 GB
LowD39
NVFP4
4
39.2 GB
MediumD40
Q4_K_M
4
42.7 GB
MediumC40
Q5_K_M
5
50.4 GB
HighC41
Q6_K
6
57.4 GB
HighC42
Q8_0
8
74.9 GB
Very HighC44
F16Best for your GPU
16
143.5 GB
MaximumC47

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 B200 180GB run stabilityai japanese stablelm instruct beta 70b?

Yes, NVIDIA B200 180GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 157.4 tok/s.

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

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 70.1 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 B200 180GB?

On NVIDIA B200 180GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 157.4 tokens per second decode speed with a time-to-first-token of 1230ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA B200 180GB receives a C grade with 157.4 tok/s and 230K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 230K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GBSee all hardware for stabilityai japanese stablelm instruct beta 70b
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