Can HelpingAI 9B 200k i1 run on GTX 1080 Ti 11GB?

YES — Runs Great

C55Usable

Estimated from fit model

HelpingAI 9B 200k i1 needs ~8.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.8 GB, 52.0 tok/s, Runs well

8.8 GB required11.0 GB available

80% VRAM used

Fit status

Runs well

Decode

52.0 tok/s

TTFT

3722 ms

Safe context

49K

Memory

8.8 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsHelpingAI 9B 200k i1 on GTX 1080 Ti 11GB
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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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	52.0 tok/s	2030 ms	49K
Coding	C	Runs well	52.0 tok/s	3722 ms	49K
Agentic Coding	C	Tight fit	52.0 tok/s	5414 ms	49K
Reasoning	C	Runs well	52.0 tok/s	4399 ms	49K
RAG	C	Tight fit	52.0 tok/s	6767 ms	49K

Quantization options

How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.

Run

lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI 9B 200k i1 well

RTX 5070 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+188)

Raises estimated decode speed by about 48%.77.1 tok/s decode

Raises estimated decode speed by about 48%.

~$549 MSRP

RTX 4070 Super 12GBBest value

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 36%.70.7 tok/s decode

Raises estimated decode speed by about 36%.

~$599 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 33%.68.9 tok/s decode

Raises estimated decode speed by about 33%.

~$599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run HelpingAI 9B 200k i1?

Yes, GTX 1080 Ti 11GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 52.0 tok/s.

How much VRAM does HelpingAI 9B 200k i1 need?

HelpingAI 9B 200k i1 (9B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 9B 200k i1?

The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 9B 200k i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, HelpingAI 9B 200k i1 achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run HelpingAI 9B 200k i1 for coding?

For coding workloads, HelpingAI 9B 200k i1 on GTX 1080 Ti 11GB receives a C grade with 52.0 tok/s and 49K context.

What context window can HelpingAI 9B 200k i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, HelpingAI 9B 200k i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for HelpingAI 9B 200k i1

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