Can Starling LM 7B run on GTX 1070 8GB?

Q: What should I upgrade first if Starling LM 7B feels slow on GTX 1070 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C51Usable

Estimated from fit model

Starling LM 7B needs ~7.9 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 7.9 GB, 38.0 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

38.0 tok/s

TTFT

5091 ms

Safe context

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsStarling LM 7B 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: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	38.0 tok/s	2777 ms	8K
Coding	C	Runs with offload	38.0 tok/s	5091 ms	8K
Agentic Coding	F	Too heavy	17.5 tok/s	16126 ms	8K
Reasoning	C	Runs with offload	38.0 tok/s	6017 ms	8K
RAG	F	Too heavy	17.5 tok/s	20157 ms	8K

Quantization options

How Starling LM 7B (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run Starling LM 7B on your machine.

Run

ollama run starling-lm

Upgrade options

Hardware that runs Starling LM 7B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+104)

Raises estimated decode speed by about 32%.50.2 tok/s decode

Raises estimated decode speed by about 32%.

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+192)

Raises estimated decode speed by about 84%.69.9 tok/s decode

Raises estimated decode speed by about 84%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+32)

Raises estimated decode speed by about 28%.48.7 tok/s decode

Raises estimated decode speed by about 28%.

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

Frequently asked questions

Can GTX 1070 8GB run Starling LM 7B?

Yes, GTX 1070 8GB can run Starling LM 7B with a C grade (Runs with offload). Expected decode speed: 38.0 tok/s.

How much VRAM does Starling LM 7B need?

Starling LM 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Starling LM 7B?

The recommended quantization for Starling LM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Starling LM 7B run at on GTX 1070 8GB?

On GTX 1070 8GB, Starling LM 7B achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5091ms using Q4_K_M quantization.

Can GTX 1070 8GB run Starling LM 7B for coding?

For coding workloads, Starling LM 7B on GTX 1070 8GB receives a C grade with 38.0 tok/s and 8K context.

What context window can Starling LM 7B use on GTX 1070 8GB?

On GTX 1070 8GB, Starling LM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Starling LM 7B feels slow on GTX 1070 8GB?

See all results for GTX 1070 8GB See all hardware for Starling LM 7B

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