Can japanese stablelm instruct gamma 7B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C45Usable

Estimated from fit model

japanese stablelm instruct gamma 7B needs ~14.3 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 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) — 14.3 GB, 98.0 tok/s, Runs well

14.3 GB required80.0 GB available

18% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

1.3M

Memory

14.3 GB / 80.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	1.3M
Coding	C	Runs well	98.0 tok/s	1976 ms	1.3M
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	1.3M
Reasoning	C	Runs well	98.0 tok/s	2335 ms	1.3M
RAG	C	Runs well	98.0 tok/s	3592 ms	1.3M

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run japanese stablelm instruct gamma 7B on your machine.

Run

lms load hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf && lms server start

Upgrade options

Hardware that runs japanese stablelm instruct gamma 7B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run japanese stablelm instruct gamma 7B?

Yes, NVIDIA H100 PCIe 80GB can run japanese stablelm instruct gamma 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does japanese stablelm instruct gamma 7B need?

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for japanese stablelm instruct gamma 7B?

The recommended quantization for japanese stablelm instruct gamma 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will japanese stablelm instruct gamma 7B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, japanese stablelm instruct gamma 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on NVIDIA H100 PCIe 80GB receives a C grade with 98.0 tok/s and 1.3M context.

What context window can japanese stablelm instruct gamma 7B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, japanese stablelm instruct gamma 7B can safely use up to 1.3M 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 japanese stablelm instruct gamma 7B

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