willitrun·ai

Can HelpingAI2 9B run on RTX 4070 Ti Super 16GB?

YES — Runs Great

C54Usable
Estimated from fit model

HelpingAI2 9B needs ~9.3 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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, 97.9 tok/s, Runs well
9.3 GB required16.0 GB available
58% VRAM used

Fit status

Runs well

Decode

97.9 tok/s

TTFT

1977 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 feelsHelpingAI2 9B on RTX 4070 Ti Super 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: 97.9 tok/s decode · 2.0s TTFT (warm) · 245 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well97.9 tok/s1078 ms117K
CodingCRuns well97.9 tok/s1977 ms117K
Agentic CodingBRuns well97.9 tok/s2876 ms117K
ReasoningCRuns well97.9 tok/s2337 ms117K
RAGBRuns well97.9 tok/s3595 ms117K

Inference speed

HelpingAI2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M126.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M126.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M125.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M119.3Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M111.3Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M101.4Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M84.5Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M80.1Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M68.9Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M68.3Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M68.3Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M43.7Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M43.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M40.1Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M35.2Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M23.4Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How HelpingAI2 9B (9B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC47
Q3_K_S
3
4.4 GB
LowC48
NVFP4
4
5.0 GB
MediumC48
Q4_K_M
4
5.5 GB
MediumC49
Q5_K_M
5
6.5 GB
HighC50
Q6_K
6
7.4 GB
HighC51
Q8_0Best for your GPU
8
9.6 GB
Very HighC51
F16
16
18.5 GB
MaximumF0

Get started

Copy-paste commands to run HelpingAI2 9B on your machine.

Run

lms load hf-bartowski--helpingai2-9b-gguf && lms server start

Frequently asked questions

Can RTX 4070 Ti Super 16GB run HelpingAI2 9B?

Yes, RTX 4070 Ti Super 16GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 97.9 tok/s.

How much VRAM does HelpingAI2 9B need?

HelpingAI2 9B (9B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 9B?

The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2 9B run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, HelpingAI2 9B achieves approximately 97.9 tokens per second decode speed with a time-to-first-token of 1977ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run HelpingAI2 9B for coding?

For coding workloads, HelpingAI2 9B on RTX 4070 Ti Super 16GB receives a C grade with 97.9 tok/s and 117K context.

What context window can HelpingAI2 9B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, HelpingAI2 9B 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 4070 Ti Super 16GBSee all hardware for HelpingAI2 9B
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<iframe src="https://willitrunai.com/embed/hf-bartowski--helpingai2-9b-gguf-on-rtx-4070-ti-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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