Can HelpingAI2.5 5B i1 run on RTX 4080 Super 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

HelpingAI2.5 5B i1 needs ~6.1 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 6.1 GB, 80.0 tok/s, Runs well

6.1 GB required16.0 GB available

38% VRAM used

Fit status

Runs well

Decode

80.0 tok/s

TTFT

2420 ms

Safe context

285K

Memory

6.1 GB / 16.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 5B i1 on RTX 4080 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: 80.0 tok/s decode · 2.4s TTFT (warm) · 200 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	80.0 tok/s	1320 ms	285K
Coding	C	Runs well	80.0 tok/s	2420 ms	285K
Agentic Coding	C	Runs well	80.0 tok/s	3520 ms	285K
Reasoning	C	Runs well	80.0 tok/s	2860 ms	285K
RAG	C	Runs well	80.0 tok/s	4400 ms	285K

Inference speed

HelpingAI2.5 5B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI2.5 5B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~95 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	95.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	80.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	80.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	70.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	70.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	70.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	70.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	70.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	70.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	70.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	70.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	70.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	70.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	65.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	63.4	Fits

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 HelpingAI2.5 5B i1 (5B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	C46
Q3_K_S	3	2.5 GB	Low	C46
NVFP4	4	2.8 GB	Medium	C46
Q4_K_M	4	3.1 GB	Medium	C47
Q5_K_M	5	3.6 GB	High	C47
Q6_K	6	4.1 GB	High	C47
Q8_0	8	5.4 GB	Very High	C49
F16Best for your GPU	16	10.3 GB	Maximum	C50

Get started

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

Run

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

Frequently asked questions

Can RTX 4080 Super 16GB run HelpingAI2.5 5B i1?

Yes, RTX 4080 Super 16GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 80.0 tok/s.

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

HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.

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

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

What speed will HelpingAI2.5 5B i1 run at on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, HelpingAI2.5 5B i1 achieves approximately 80.0 tokens per second decode speed with a time-to-first-token of 2420ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run HelpingAI2.5 5B i1 for coding?

For coding workloads, HelpingAI2.5 5B i1 on RTX 4080 Super 16GB receives a C grade with 80.0 tok/s and 285K context.

What context window can HelpingAI2.5 5B i1 use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, HelpingAI2.5 5B i1 can safely use up to 285K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4080 Super 16GB See all hardware for HelpingAI2.5 5B i1

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