Can HelpingAI 15B i1 run on GTX 1070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

HelpingAI 15B i1 needs ~12.9 GB but GTX 1070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 12.9 GB, exceeds 8.0 GB available

12.9 GB required8.0 GB available

161% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.2 tok/s

TTFT

45974 ms

Safe context

Memory

12.9 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsHelpingAI 15B i1 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: 4.2 tok/s decode · 46.0s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.9 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.9 tok/s	21479 ms	4K
Coding	F	Too heavy	4.2 tok/s	45974 ms	4K
Agentic Coding	F	Too heavy	3.2 tok/s	88511 ms	4K
Reasoning	F	Too heavy	4.2 tok/s	54332 ms	4K
RAG	F	Too heavy	3.2 tok/s	110639 ms	4K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	F0
Q3_K_S	3	7.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs HelpingAI 15B i1 well

RTX 3060 12GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.15.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 274%.

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.30.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can GTX 1070 8GB run HelpingAI 15B i1?

No, HelpingAI 15B i1 requires more memory than GTX 1070 8GB provides.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 15B i1?

The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 15B i1 run at on GTX 1070 8GB?

On GTX 1070 8GB, HelpingAI 15B i1 achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 45974ms using Q4_K_M quantization.

Can GTX 1070 8GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on GTX 1070 8GB receives a F grade with 4.2 tok/s and 4K context.

What context window can HelpingAI 15B i1 use on GTX 1070 8GB?

On GTX 1070 8GB, HelpingAI 15B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if HelpingAI 15B i1 feels slow on GTX 1070 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1070 8GB See all hardware for HelpingAI 15B i1

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