Can stablelm 3b 4e1t run on GTX 1070 8GB?

YES — Runs Great

C50Usable

Estimated from fit model

stablelm 3b 4e1t needs ~4.2 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 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) — 4.2 GB, 42.0 tok/s, Runs well

4.2 GB required8.0 GB available

53% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

190K

Memory

4.2 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 3b 4e1t on GTX 1070 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	C	Runs well	42.0 tok/s	2514 ms	190K
Coding	C	Runs well	42.0 tok/s	4610 ms	190K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	190K
Reasoning	C	Runs well	42.0 tok/s	5448 ms	190K
RAG	C	Runs well	42.0 tok/s	8381 ms	190K

Quantization options

How stablelm 3b 4e1t (3B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C50
Q3_K_S	3	1.5 GB	Low	C50
NVFP4	4	1.7 GB	Medium	C51
Q4_K_M	4	1.8 GB	Medium	C51
Q5_K_M	5	2.2 GB	High	C52
Q6_K	6	2.5 GB	High	C52
Q8_0Best for your GPU	8	3.2 GB	Very High	C53
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run stablelm 3b 4e1t on your machine.

Run

lms load hf-afrideva--stablelm-3b-4e1t-gguf && lms server start

Frequently asked questions

Can GTX 1070 8GB run stablelm 3b 4e1t?

Yes, GTX 1070 8GB can run stablelm 3b 4e1t with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does stablelm 3b 4e1t need?

stablelm 3b 4e1t (3B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 3b 4e1t?

The recommended quantization for stablelm 3b 4e1t is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 3b 4e1t run at on GTX 1070 8GB?

On GTX 1070 8GB, stablelm 3b 4e1t achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can GTX 1070 8GB run stablelm 3b 4e1t for coding?

For coding workloads, stablelm 3b 4e1t on GTX 1070 8GB receives a C grade with 42.0 tok/s and 190K context.

What context window can stablelm 3b 4e1t use on GTX 1070 8GB?

On GTX 1070 8GB, stablelm 3b 4e1t can safely use up to 190K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1070 8GB See all hardware for stablelm 3b 4e1t

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-afrideva--stablelm-3b-4e1t-gguf-on-gtx-1070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

42.0 tok/s

2514 ms

190K

Coding

Runs well

42.0 tok/s

4610 ms

190K

Agentic Coding

Runs well

42.0 tok/s

6705 ms

190K

Reasoning

Runs well

42.0 tok/s

5448 ms

190K

RAG

Runs well

42.0 tok/s

8381 ms

190K

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.2 GB

Low

C50

Q3_K_S

1.5 GB

Low

C50

NVFP4

1.7 GB

Medium

C51

Q4_K_M

1.8 GB

Medium

C51

Q5_K_M

2.2 GB

High

C52

Q6_K

2.5 GB

High

C52

Q8_0Best for your GPU

3.2 GB

Very High

C53

F16

6.1 GB

Maximum

Frequently asked questions

Can GTX 1070 8GB run stablelm 3b 4e1t?

Yes, GTX 1070 8GB can run stablelm 3b 4e1t with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does stablelm 3b 4e1t need?

stablelm 3b 4e1t (3B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 3b 4e1t?

The recommended quantization for stablelm 3b 4e1t is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 3b 4e1t run at on GTX 1070 8GB?

On GTX 1070 8GB, stablelm 3b 4e1t achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can GTX 1070 8GB run stablelm 3b 4e1t for coding?

For coding workloads, stablelm 3b 4e1t on GTX 1070 8GB receives a C grade with 42.0 tok/s and 190K context.

What context window can stablelm 3b 4e1t use on GTX 1070 8GB?

On GTX 1070 8GB, stablelm 3b 4e1t can safely use up to 190K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.