Can HelpingAI2.5 10B i1 run on GTX 1070 Ti 8GB?

Q: What should I upgrade first if HelpingAI2.5 10B i1 feels slow on GTX 1070 Ti 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D37Poor

Estimated from fit model

HelpingAI2.5 10B i1 needs ~9.0 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 9.0 GB, 14.0 tok/s, Very compromised (needs ~0.7 GB host RAM)

9.0 GB required8.0 GB available

113% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

14.0 tok/s

TTFT

13788 ms

Safe context

Memory

9.0 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on GTX 1070 Ti 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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	16.3 tok/s	6484 ms	4K
Coding	D	Very compromised	14.0 tok/s	13788 ms	4K
Agentic Coding	F	Too heavy	10.7 tok/s	26262 ms	4K
Reasoning	D	Very compromised	14.0 tok/s	16295 ms	4K
RAG	F	Too heavy	10.7 tok/s	32827 ms	4K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C53
Q3_K_SBest for your GPU	3	4.9 GB	Low	C52

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2.5 10B i1 well

RTX 3060 12GBBudget pick

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.32.7 tok/s decode

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

Raises estimated decode speed by about 134%.

~$329 MSRP

RTX 5060 Ti 16GBBest value

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.45.5 tok/s decode

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

Raises estimated decode speed by about 225%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.31.7 tok/s decode

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

Raises estimated decode speed by about 126%.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run HelpingAI2.5 10B i1?

Yes, GTX 1070 Ti 8GB can run HelpingAI2.5 10B i1 with a D grade (Very compromised). Expected decode speed: 14.0 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, HelpingAI2.5 10B i1 achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13788ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on GTX 1070 Ti 8GB receives a D grade with 14.0 tok/s and 4K context.

What context window can HelpingAI2.5 10B i1 use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, HelpingAI2.5 10B 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 HelpingAI2.5 10B i1 feels slow on GTX 1070 Ti 8GB?

See all results for GTX 1070 Ti 8GB See all hardware for HelpingAI2.5 10B i1

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