Will It Run AI

Can stablelm 2 zephyr 1 6b run on RTX 6000 Ada 48GB?

YES — Runs Great

C46Usable
Estimated from fit model

stablelm 2 zephyr 1 6b needs ~10.4 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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, 84.0 tok/s, Runs well
10.4 GB required48.0 GB available
22% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

872K

Memory

10.4 GB / 48.0 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1 6b on RTX 6000 Ada 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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well84.0 tok/s1257 ms872K
CodingCRuns well84.0 tok/s2305 ms872K
Agentic CodingCRuns well84.0 tok/s3352 ms872K
ReasoningCRuns well84.0 tok/s2724 ms872K
RAGCRuns well84.0 tok/s4190 ms872K

Quantization options

How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC41
Q3_K_S
3
2.9 GB
LowC41
NVFP4
4
3.4 GB
MediumC41
Q4_K_M
4
3.7 GB
MediumC41
Q5_K_M
5
4.3 GB
HighC42
Q6_K
6
4.9 GB
HighC42
Q8_0
8
6.4 GB
Very HighC42
F16Best for your GPU
16
12.3 GB
MaximumC43

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

Opciones de mejora

Hardware que ejecuta bien stablelm 2 zephyr 1 6b

MacBook Pro M4 Max 96GBOpción económica
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.84 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejor relación calidad-precio
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.84 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can RTX 6000 Ada 48GB run stablelm 2 zephyr 1 6b?

Yes, RTX 6000 Ada 48GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 84.0 tok/s.

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

stablelm 2 zephyr 1 6b (6B 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, stablelm 2 zephyr 1 6b achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run stablelm 2 zephyr 1 6b for coding?

For coding workloads, stablelm 2 zephyr 1 6b on RTX 6000 Ada 48GB receives a C grade with 84.0 tok/s and 872K context.

What context window can stablelm 2 zephyr 1 6b use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, stablelm 2 zephyr 1 6b can safely use up to 872K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GBSee all hardware for stablelm 2 zephyr 1 6b
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