Can stablelm 2 1 6b chat imatrix run on NVIDIA V100 32GB?

YES — Runs Great

C47Usable

Estimated from fit model

stablelm 2 1 6b chat imatrix needs ~8.8 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~84 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) — 8.8 GB, 84.0 tok/s, Runs well

8.8 GB required32.0 GB available

28% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

545K

Memory

8.8 GB / 32.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsstablelm 2 1 6b chat imatrix on NVIDIA V100 32GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	84.0 tok/s	1257 ms	545K
Coding	C	Runs well	84.0 tok/s	2305 ms	545K
Agentic Coding	C	Runs well	84.0 tok/s	3352 ms	545K
Reasoning	C	Runs well	84.0 tok/s	2724 ms	545K
RAG	C	Runs well	84.0 tok/s	4190 ms	545K

Quantization options

How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C43
Q3_K_S	3	2.9 GB	Low	C43
NVFP4	4

Get started

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

Run

lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server start

Upgrade options

Hardware that runs stablelm 2 1 6b chat imatrix well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.84 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run stablelm 2 1 6b chat imatrix?

Yes, NVIDIA V100 32GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 84.0 tok/s.

How much VRAM does stablelm 2 1 6b chat imatrix need?

stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 1 6b chat imatrix?

The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 1 6b chat imatrix run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, stablelm 2 1 6b chat imatrix achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run stablelm 2 1 6b chat imatrix for coding?

For coding workloads, stablelm 2 1 6b chat imatrix on NVIDIA V100 32GB receives a C grade with 84.0 tok/s and 545K context.

What context window can stablelm 2 1 6b chat imatrix use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, stablelm 2 1 6b chat imatrix can safely use up to 545K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for stablelm 2 1 6b chat imatrix

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