Can granite embedding 107m multilingual run on NVIDIA H100 PCIe 80GB?

Q: What should I upgrade first if granite embedding 107m multilingual feels slow on NVIDIA H100 PCIe 80GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

D33Poor

Estimated from fit model

granite embedding 107m multilingual needs ~9.4 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Memory bandwidth

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) — 9.4 GB, 2.0 tok/s, Runs well

9.4 GB required80.0 GB available

12% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

11.3M

Memory

9.4 GB / 80.0 GB

Memory breakdown

Weights0.1 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsgranite embedding 107m multilingual 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Runs well	2.0 tok/s	52800 ms	5.7M
Coding	D	Runs well	2.0 tok/s	96800 ms	11.3M
Agentic Coding	D	Runs well	2.0 tok/s	140800 ms	22.6M
Reasoning	D	Runs well	2.0 tok/s	114400 ms	11.3M
RAG	D	Runs well	2.0 tok/s	176000 ms	22.6M

Quantization options

How granite embedding 107m multilingual (0.10700000077486038B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.0 GB	Low	D40
Q3_K_S	3	0.1 GB	Low	D40
NVFP4	4

Get started

Copy-paste commands to run granite embedding 107m multilingual on your machine.

Run

lms load hf-bartowski--granite-embedding-107m-multilingual-gguf && lms server start

Upgrade options

Hardware that runs granite embedding 107m multilingual well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 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.2 tok/s decode

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

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run granite embedding 107m multilingual?

Yes, NVIDIA H100 PCIe 80GB can run granite embedding 107m multilingual with a D grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does granite embedding 107m multilingual need?

granite embedding 107m multilingual (0.10700000077486038B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for granite embedding 107m multilingual?

The recommended quantization for granite embedding 107m multilingual is Q4_K_M, which balances quality and memory efficiency.

What speed will granite embedding 107m multilingual run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, granite embedding 107m multilingual achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run granite embedding 107m multilingual for coding?

For coding workloads, granite embedding 107m multilingual on NVIDIA H100 PCIe 80GB receives a D grade with 2.0 tok/s and 11.3M context.

What context window can granite embedding 107m multilingual use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, granite embedding 107m multilingual can safely use up to 11.3M 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 granite embedding 107m multilingual

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