Can stablelm 2 zephyr 1 6b run on NVIDIA L40 48GB?

YES — Runs Great

C46Usable

Estimated from fit model

stablelm 2 zephyr 1 6b needs ~10.1 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 10.1 GB, 96.0 tok/s, Runs well

10.1 GB required48.0 GB available

21% VRAM used

Fit status

Runs well

Decode

96.0 tok/s

TTFT

2017 ms

Safe context

879K

Memory

10.1 GB / 48.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1 6b on NVIDIA L40 48GB
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: 96.0 tok/s decode · 2.0s TTFT (warm) · 240 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	96.0 tok/s	1100 ms	879K
Coding	C	Runs well	96.0 tok/s	2017 ms	879K
Agentic Coding	C	Runs well	96.0 tok/s	2933 ms	879K
Reasoning	C	Runs well	84.0 tok/s	2724 ms	879K
RAG	C	Runs well	96.0 tok/s	3667 ms	879K

Quantization options

How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C41
Q3_K_S	3	2.9 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run stablelm 2 zephyr 1 6b on your machine.

Run

lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server start

Upgrade options

Hardware that runs stablelm 2 zephyr 1 6b well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.84 tok/s decode

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.84 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run stablelm 2 zephyr 1 6b?

Yes, NVIDIA L40 48GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 96.0 tok/s.

How much VRAM does stablelm 2 zephyr 1 6b need?

stablelm 2 zephyr 1 6b (6B parameters) requires approximately 10.1 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 L40 48GB?

On NVIDIA L40 48GB, stablelm 2 zephyr 1 6b achieves approximately 96.0 tokens per second decode speed with a time-to-first-token of 2017ms using Q4_K_M quantization.

Can NVIDIA L40 48GB run stablelm 2 zephyr 1 6b for coding?

For coding workloads, stablelm 2 zephyr 1 6b on NVIDIA L40 48GB receives a C grade with 96.0 tok/s and 879K context.

What context window can stablelm 2 zephyr 1 6b use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, stablelm 2 zephyr 1 6b can safely use up to 879K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L40 48GB See all hardware for stablelm 2 zephyr 1 6b

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