Can stablelm 2 zephyr 1.6b run on NVIDIA H100 80GB?

YES — Runs Great

D40Poor

Estimated from fit model

stablelm 2 zephyr 1.6b needs ~10.4 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~22 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) — 10.4 GB, 22.4 tok/s, Runs well

10.4 GB required80.0 GB available

13% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

6.0M

Memory

10.4 GB / 80.0 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1.6b on NVIDIA H100 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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	D	Runs well	22.4 tok/s	4714 ms	5.6M
Coding	D	Runs well	22.4 tok/s	8643 ms	6.0M
Agentic Coding	D	Runs well	22.4 tok/s	12571 ms	6.0M
Reasoning	D	Runs well	22.4 tok/s	10214 ms	6.0M
RAG	D	Runs well	22.4 tok/s	15714 ms	6.0M

Quantization options

How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D39
Q3_K_S	3	0.8 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.

Run

lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server start

Upgrade options

Hardware that runs stablelm 2 zephyr 1.6b well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.22.4 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.22.4 tok/s decode

~$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.22.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA H100 80GB run stablelm 2 zephyr 1.6b?

Yes, NVIDIA H100 80GB can run stablelm 2 zephyr 1.6b with a D grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does stablelm 2 zephyr 1.6b need?

stablelm 2 zephyr 1.6b (1.600000023841858B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 zephyr 1.6b?

The recommended quantization for stablelm 2 zephyr 1.6b is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 zephyr 1.6b run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, stablelm 2 zephyr 1.6b achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run stablelm 2 zephyr 1.6b for coding?

For coding workloads, stablelm 2 zephyr 1.6b on NVIDIA H100 80GB receives a D grade with 22.4 tok/s and 6.0M context.

What context window can stablelm 2 zephyr 1.6b use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, stablelm 2 zephyr 1.6b can safely use up to 6.0M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for stablelm 2 zephyr 1.6b

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