Can HelpingAI2 6B run on GTX 1070 8GB?

YES — Runs Great

C54Usable

Estimated from fit model

HelpingAI2 6B needs ~6.4 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~41 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) — 6.4 GB, 41.3 tok/s, Runs well

6.4 GB required8.0 GB available

80% VRAM used

Fit status

Runs well

Decode

41.3 tok/s

TTFT

4691 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 feelsHelpingAI2 6B 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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 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	41.3 tok/s	2559 ms	53K
Coding	C	Runs well	41.3 tok/s	4691 ms	53K
Agentic Coding	C	Tight fit	41.3 tok/s	6824 ms	53K
Reasoning	C	Runs well	41.3 tok/s	5544 ms	53K
RAG	C	Tight fit	41.3 tok/s	8530 ms	53K

Quantization options

How HelpingAI2 6B (6B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C52
Q3_K_S	3	2.9 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2 6B on your machine.

Run

lms load hf-helpingai--helpingai2-6b && lms server start

Upgrade options

Hardware that runs HelpingAI2 6B well

RTX 3080 10GBBudget pick

10 GB VRAM (+2)760 GB/s (+504)

Raises estimated decode speed by about 103%.84 tok/s decode

Raises estimated decode speed by about 103%.

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

~$699 MSRP

GTX 1080 Ti 11GBBest value

11 GB VRAM (+3)484 GB/s (+228)

Raises estimated decode speed by about 89%.78 tok/s decode

Raises estimated decode speed by about 89%.

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

~$699 MSRP

RTX 2080 Ti 11GBNVIDIA upgrade

11 GB VRAM (+3)616 GB/s (+360)

Raises estimated decode speed by about 103%.84 tok/s decode

Raises estimated decode speed by about 103%.

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

~$999 MSRP

Frequently asked questions

Can GTX 1070 8GB run HelpingAI2 6B?

Yes, GTX 1070 8GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 41.3 tok/s.

How much VRAM does HelpingAI2 6B need?

HelpingAI2 6B (6B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 6B?

The recommended quantization for HelpingAI2 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2 6B run at on GTX 1070 8GB?

On GTX 1070 8GB, HelpingAI2 6B achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4691ms using Q4_K_M quantization.

Can GTX 1070 8GB run HelpingAI2 6B for coding?

For coding workloads, HelpingAI2 6B on GTX 1070 8GB receives a C grade with 41.3 tok/s and 53K context.

What context window can HelpingAI2 6B use on GTX 1070 8GB?

On GTX 1070 8GB, HelpingAI2 6B can safely use up to 53K 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 HelpingAI2 6B

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