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URL: https://willitrunai.com/can-run/hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-rtx-2070-super-8gb

⇱ stablelm 2 1 6b chat imatrix on RTX 2070 Super 8GB? YES

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

YES — Runs Great

B56Good
○Estimated from fit model

stablelm 2 1 6b chat imatrix 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
Share:

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 1 6b chat imatrix 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well74.7 tok/s1414 ms53K
CodingBRuns well74.7 tok/s2593 ms53K
Agentic CodingCTight fit74.7 tok/s3771 ms53K
ReasoningBRuns well74.7 tok/s3064 ms53K
RAGCTight fit74.7 tok/s4714 ms53K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC52
Q3_K_S
3
2.9 GB
LowC53
NVFP4
4
3.4 GB
MediumC53
Q4_K_M
4
3.7 GB
MediumC53
Q5_K_M
5
4.3 GB
HighC53
Q6_KBest for your GPU
6
4.9 GB
HighC53
Q8_0
8
6.4 GB
Very HighF0
F16
16
12.3 GB
MaximumF0

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

Frequently asked questions

See all results for RTX 2070 Super 8GBSee all hardware for stablelm 2 1 6b chat imatrix