HelpingAI2.5 5B i1 needs ~6.1 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.
Operating mode
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
Fit status
Runs well
Decode
80.0 tok/s
TTFT
2420 ms
Safe context
285K
Memory
6.1 GB / 16.0 GB
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.
| 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
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.
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on RTX 6000 Ada Laptop 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 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startYes, RTX 6000 Ada Laptop 16GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 80.0 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 6000 Ada Laptop 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.
For coding workloads, HelpingAI2.5 5B i1 on RTX 6000 Ada Laptop 16GB receives a C grade with 80.0 tok/s and 285K context.
On RTX 6000 Ada Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-5b-i1-gguf-on-rtx-6000-ada-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: