Can stablelm 2 zephyr 1 6b run on RTX 2070 Super 8GB?

YES — Runs Great

B56Good

Estimated from fit model

stablelm 2 zephyr 1 6b needs ~6.4 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 6.4 GB, 74.7 tok/s, Runs well

6.4 GB required8.0 GB available

80% VRAM used

Fit status

Runs well

Decode

74.7 tok/s

TTFT

2593 ms

Safe context

53K

Memory

6.4 GB / 8.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1 6b on RTX 2070 Super 8GB
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: 74.7 tok/s decode · 2.6s TTFT (warm) · 187 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	74.7 tok/s	1414 ms	53K
Coding	B	Runs well	74.7 tok/s	2593 ms	53K
Agentic Coding	C	Tight fit	74.7 tok/s	3771 ms	53K
Reasoning	B	Runs well	74.7 tok/s	3064 ms	53K
RAG	C	Tight fit	74.7 tok/s	4714 ms	53K

Quantization options

How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C52
Q3_K_S	3	2.9 GB	Low	C54
NVFP4	4	3.4 GB	Medium	C54
Q4_K_M	4	3.7 GB	Medium	C53
Q5_K_M	5	4.3 GB	High	C53
Q6_KBest for your GPU	6	4.9 GB	High	C53
Q8_0	8	6.4 GB	Very High	F0
F16	16	12.3 GB	Maximum	F0

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

Frequently asked questions

Can RTX 2070 Super 8GB run stablelm 2 zephyr 1 6b?

Yes, RTX 2070 Super 8GB can run stablelm 2 zephyr 1 6b with a B grade (Runs well). Expected decode speed: 74.7 tok/s.

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

stablelm 2 zephyr 1 6b (6B parameters) requires approximately 6.4 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 2070 Super 8GB?

On RTX 2070 Super 8GB, stablelm 2 zephyr 1 6b achieves approximately 74.7 tokens per second decode speed with a time-to-first-token of 2593ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run stablelm 2 zephyr 1 6b for coding?

For coding workloads, stablelm 2 zephyr 1 6b on RTX 2070 Super 8GB receives a B grade with 74.7 tok/s and 53K context.

What context window can stablelm 2 zephyr 1 6b use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, stablelm 2 zephyr 1 6b can safely use up to 53K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 Super 8GB See all hardware for stablelm 2 zephyr 1 6b

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