Can HelpingAI 9B 200k i1 run on RTX 2000 Ada 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

HelpingAI 9B 200k i1 needs ~9.3 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 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) — 9.3 GB, 39.9 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

39.9 tok/s

TTFT

4856 ms

Safe context

117K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI 9B 200k i1 on RTX 2000 Ada 16GB
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: 39.9 tok/s decode · 4.9s TTFT (warm) · 100 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	39.9 tok/s	2649 ms	117K
Coding	C	Runs well	39.9 tok/s	4856 ms	117K
Agentic Coding	C	Runs well	39.9 tok/s	7063 ms	117K
Reasoning	C	Runs well	39.9 tok/s	5739 ms	117K
RAG	C	Runs well	39.9 tok/s	8829 ms	117K

Quantization options

How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C47
Q3_K_S	3	4.4 GB	Low	C48
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

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 119%.87.4 tok/s decode

Raises estimated decode speed by about 119%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 128%.90.9 tok/s decode

Raises estimated decode speed by about 128%.

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

~$2,000 MSRP

Frequently asked questions

Can RTX 2000 Ada 16GB run HelpingAI 9B 200k i1?

Yes, RTX 2000 Ada 16GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 39.9 tok/s.

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

HelpingAI 9B 200k i1 (9B parameters) requires approximately 9.3 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 RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, HelpingAI 9B 200k i1 achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run HelpingAI 9B 200k i1 for coding?

For coding workloads, HelpingAI 9B 200k i1 on RTX 2000 Ada 16GB receives a C grade with 39.9 tok/s and 117K context.

What context window can HelpingAI 9B 200k i1 use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, HelpingAI 9B 200k i1 can safely use up to 117K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2000 Ada 16GB See all hardware for HelpingAI 9B 200k i1

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