Can stabilityai japanese stablelm instruct beta 70b run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C54Usable

Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~60.1 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~39 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) — 60.1 GB, 39.3 tok/s, Runs well

60.1 GB required80.0 GB available

75% VRAM used

Fit status

Runs well

Decode

39.3 tok/s

TTFT

4921 ms

Safe context

55K

Memory

60.1 GB / 80.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm instruct beta 70b on NVIDIA H100 PCIe 80GB
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: 39.3 tok/s decode · 4.9s TTFT (warm) · 98 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	39.3 tok/s	2684 ms	55K
Coding	C	Runs well	39.3 tok/s	4921 ms	55K
Agentic Coding	C	Tight fit	39.3 tok/s	7157 ms	55K
Reasoning	C	Runs well	39.3 tok/s	5815 ms	55K
RAG	C	Tight fit	39.3 tok/s	8947 ms	55K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA H100 PCIe 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

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

Upgrade options

Hardware that runs stabilityai japanese stablelm instruct beta 70b well

NVIDIA H20 96GBBudget pick

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.75.9 tok/s decode

Raises estimated decode speed by about 93%.

~$12,000 MSRP

NVIDIA GH200 96GBBest value

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.75.9 tok/s decode

Raises estimated decode speed by about 93%.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run stabilityai japanese stablelm instruct beta 70b?

Yes, NVIDIA H100 PCIe 80GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 39.3 tok/s.

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

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 60.1 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 39.3 tokens per second decode speed with a time-to-first-token of 4921ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA H100 PCIe 80GB receives a C grade with 39.3 tok/s and 55K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 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.

See all results for NVIDIA H100 PCIe 80GB See all hardware for stabilityai japanese stablelm instruct beta 70b

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<iframe src="https://willitrunai.com/embed/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

39.3 tok/s

2684 ms

55K

Coding

Runs well

39.3 tok/s

4921 ms

55K

Agentic Coding

Tight fit

39.3 tok/s

7157 ms

55K

Reasoning

Runs well

39.3 tok/s

5815 ms

55K

RAG

Tight fit

39.3 tok/s

8947 ms

55K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA H100 PCIe 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