MacBook Pro M4 Max 36GBOpção econômica
36 GB Unified (+12)
C
This setup is broadly balanced for this model.76.9 tok/s decodificação
~$2,499 MSRP
Can HelpingAI2 6B i1 run on RTX 3090 24GB?
YES — Runs Great
C48Usable
Estimated from fit model
HelpingAI2 6B i1 needs ~8.0 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~84 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 8.0 GB, 84.0 tok/s, Runs well
8.0 GB required24.0 GB available
33% VRAM used
Fit status
Runs well
Decode
84.0 tok/s
TTFT
2305 ms
Safe context
381K
Memory
8.0 GB / 24.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsHelpingAI2 6B i1 on RTX 3090 24GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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 | 84.0 tok/s | 1257 ms | 381K |
| Coding | C | Runs well | 84.0 tok/s | 2305 ms | 381K |
| Agentic Coding | C | Runs well | 84.0 tok/s | 3352 ms | 381K |
| Reasoning | C | Runs well | 84.0 tok/s | 2724 ms | 381K |
| RAG | C | Runs well | 84.0 tok/s | 4190 ms | 381K |
Inference speed
HelpingAI2 6B i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for HelpingAI2 6B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~114 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 | 114.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 84.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 84.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 65.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 64.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 60.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 54.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 52.8 | 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 6B i1 (6B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 | 3.4 GB | Medium | C44 |
Q4_K_M | 4 | 3.7 GB | Medium | C44 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem HelpingAI2 6B i1
Frequently asked questions
Can RTX 3090 24GB run HelpingAI2 6B i1?
Yes, RTX 3090 24GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 84.0 tok/s.
How much VRAM does HelpingAI2 6B i1 need?
HelpingAI2 6B i1 (6B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2 6B i1?
The recommended quantization for HelpingAI2 6B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2 6B i1 run at on RTX 3090 24GB?
On RTX 3090 24GB, HelpingAI2 6B i1 achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.
Can RTX 3090 24GB run HelpingAI2 6B i1 for coding?
For coding workloads, HelpingAI2 6B i1 on RTX 3090 24GB receives a C grade with 84.0 tok/s and 381K context.
What context window can HelpingAI2 6B i1 use on RTX 3090 24GB?
On RTX 3090 24GB, HelpingAI2 6B i1 can safely use up to 381K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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